{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# End-to-End Example\n",
    "\n",
    "We will be looking at Radio Data System (RDS), which is a communications protocol for embedding small amounts of information in FM radio broadcasts, such as station and song name. We will have to demodulate FM, frequency shift, filter, decimate, resample, synchronize, decode, and parse the bytes. An example IQ file is provided for testing purposes or if you don’t have an SDR handy.\n",
    "\n",
    "## Introduction to FM Radio and RDS\n",
    "\n",
    "To understand RDS we must first review FM radio broadcasts and how their signals are structured. You are probably familiar with the audio portion of FM signals, which are simply audio signals frequency modulated and transmitted at center frequencies corresponding to the station’s name, e.g., “WPGC 95.5 FM” is centered at exactly 95.5 MHz. In addition to the audio portion, each FM broadcast contains some other components that are frequency modulated along with the audio. Instead of just Googling the signal structure, let’s take a look at the power spectral density (PSD) of an example FM signal, after the FM demodulation. We only view the positive portion because the output of FM demodulation is a real signal, even though the input was complex (we will view the code to perform this demodulation shortly).\n",
    "\n",
    "The spectrum would looks like:\n",
    "\n",
    "![Alt text](https://pysdr.org/_images/fm_psd.svg)\n",
    "\n",
    "![Alt text](https://pysdr.org/_images/fm_before_demod.svg)\n",
    "\n",
    "Carson’s bandwidth rule applied to FM tells us that FM stations occupy roughly 250 kHz of spectrum, which is why we usually sample at 250 kHz (recall that when using quadrature/IQ sampling, your received bandwidth equals your sampling rate).\n",
    "\n",
    "We come to RDS, which is the focus of the rest of this chapter. As we can see in our first PSD, RDS is roughly 4 kHz in bandwidth (before it gets FM modulated), and sits in between the stereo audio and DirectBand signal. It is a low data rate digital communications protocol that allows FM stations to include station identification, program information, time, and other miscellaneous information alongside the audio. The RDS standard is published as IEC standard 62106 and can be found here."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The RDS Signal\n",
    "\n",
    "### Transmit Side\n",
    "\n",
    "The RDS information to be transmitted by the FM station (e.g., track name, etc.) is __encoded into sets of 8 bytes.__ Each set of 8 bytes, which corresponds to 64 bits, is combined with 40 “check bits” to make a single “group”. __These 104 bits are transmitted together, although there is no gap of time between groups,__ so from the receiver’s perspective it receives these bits nonstop and must determine the boundary between the groups of 104 bits. We will see more details on the encoding and message structure once we dive into the receive side.\n",
    "\n",
    "To transmit these bits wirelessly, __RDS uses BPSK__, which as we learned in the Digital Modulation chapter is a simple digital modulation scheme used to map 1’s and 0’s to the phase of a carrier. Like many BPSK-based protocols, __RDS uses differential coding__, which simply means the 1’s and 0’s of data are encoded in changes of 1’s and 0’s instead, which lets you no longer care whether you are 180 degrees out of phase (more on this later). __The BPSK symbols are transmitted at 1187.5 symbols per second, and because BPSK carries one bit per symbol, that means RDS has a raw data rate of roughly 1.2 kbps__ (including overhead). __RDS does not contain any channel coding__ (a.k.a. forward error correction), although the __data packets do contain a cyclic redundancy check (CRC)__ to know when an error occurred.\n",
    "\n",
    "The final BPSK signal is then frequency shifted up to 57 kHz and added to all the other components of the FM signal, before being FM modulated and transmitted over the air at the station’s frequency. FM radio signals are transmitted at an extremely high power compared to most other wireless communications, up to 80 kW! This is why many SDR users have an FM-reject filter (i.e., a band-stop filter) in-line with their antenna; so FM does not add interference to what they are trying to receive.\n",
    "\n",
    "### Receive Side\n",
    "\n",
    "In order to demodulate and decode RDS, we will perform the following steps:\n",
    "\n",
    "1. Receive an FM radio signal centered at the station’s frequency (or read in an IQ recording), usually at a sample rate of 250 kHz\n",
    "\n",
    "2. Demodulate the FM using what is called “quadrature demodulation”\n",
    "\n",
    "3. Frequency shift by 57 kHz so the RDS signal is centered at 0 Hz\n",
    "\n",
    "4. Low-pass filter, to filter out everything besides RDS (also acts as matched filter)\n",
    "\n",
    "5. Decimate by 10 so that we can work at a lower sample rate, since we filtered out the higher frequencies anyway\n",
    "\n",
    "6. Resample to 19 kHz which will give us an integer number of samples per symbol\n",
    "\n",
    "7. Symbol-level time synchronization, using Mueller and Muller in this example\n",
    "\n",
    "8. Fine frequency synchronization using a Costas loop\n",
    "\n",
    "9. Demodulate the BPSK to 1’s and 0’s\n",
    "\n",
    "10. Differential decoding, to undo the differential encoding that was applied\n",
    "\n",
    "11. Decoding of the 1’s and 0’s into groups of bytes\n",
    "\n",
    "12. Parsing of the groups of bytes into our final output\n",
    "\n",
    "\n",
    "While this may seem like a lot of steps, RDS is actually one of the simplest wireless digital communications protocols out there. A modern wireless protocol like WiFi or 5G requires a whole textbook to cover just the high-level PHY/MAC layer information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8a80907910>]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAGvCAYAAACn9fQdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABhpElEQVR4nO3deXhMZ/sH8O8kkUSQRIQspGJfQzSIWFoqlaAtv260iqqlVdpq2ipvi5YWbVUXr1ZrKd4qqkVrqSIVa2whdir2JYsgK7Ke3x8YmWQms+ScOct8P9c1V5uZM2fuGTPn3OdZ7kcnCIIAIiIiIo1wkjsAIiIiIjExuSEiIiJNYXJDREREmsLkhoiIiDSFyQ0RERFpCpMbIiIi0hQmN0RERKQpTG6IiIhIU1zkDkAOxcXFuHr1KqpVqwadTid3OERERGQBQRCQnZ2NwMBAODmZbp9xyOTm6tWrCAoKkjsMIiIissGlS5dQp04dk487ZHJTrVo1AHc/HE9PT5mjISIiIktkZWUhKChIfx43xSGTm/tdUZ6enkxuiIiIVMbckBIOKCYiIiJNYXJDREREmsLkhoiIiDSFyQ0RERFpCpMbIiIi0hQmN0RERKQpTG6IiIhIU5jcEBERkaYwuSEiIiJNYXJDREREmsLkhoiIiDSFyQ0RERFpCpMbIlKMnUnpOJmSJXcYRKp34OJNvPPrIVzLzpM7FFk45KrgRKQ859JzMWDeHgDA+em9ZY6GSN2e/m4XACDzdgHmDW4rczT2x5YbIlKEc+k5codApDmO+rtickNE5SouFkTb16/7L2HG36dE2x8RkTFMboiojMOXM7ArKR3n03MROnkjvtr0ryj7HfvbYfx3SxISL2WIsj+SRmrWHRSJmNQS2RvH3BBRGU/9dycAIKxudWTdKcQ3safx9uONRdt/5u0C0fZF4tqZlI4B8/aga5OaWDikvdzhENmELTcKc/hyBppO+AsPT9nEWSMku5TMO3KHQHb2085zAIC4U9cgCGy9IXVicqMwryzchzsFxbiRm6+/eiYi5UvLvoP3VhzCIQ11ub29PFHuEKiCHDU9ZXKjMDl5hfr/zy8sljESIvtSeyPBuN+PYEXCZfSZre6LkpSsB611qxOv4nZ+kYzR3LXq4GVETIvF0SuZcodCKsHkhiRTXCxg15l0ZN3h+ApSjrTsO5IMaE5K08aU26NXDLvDe36zTaZIHnh7+SEkZ97BG0sPAgBu5OZj8/FUFBbxApCMY3JjR38kXkHChRtGH0tKy8aAebtxp0A7P9bF8efx4tw9aDN5E37cdgZ3CuS/AiR1ybxdgN1nr4s6Hb39p7HoO3unprqPpHT++i25Q9AruJfM9PxmG4Yt3o9v/0mSOSJSKiY3dnL4cgbeWpaIZ76PN/r4sEX7sTPpup2jktaqg1cAAEXFAqauP4kftp6VOSL1KS4WcCM3X+4wZNN39k70/3E3fku4LPq+9503fqGhZXcKivDRn8ew/fQ1uUOpkNSsu0sKfBt7GlPXn5A5GmU7ey3Xqu2T0rLx0Z/HkJat7skETG7sxNzVT7IDzEo5fDlD7hBUZ8T/9uPhKZtMtvhJ7UrGbVle975z6XcPzGsOX5U1Dq1YsPMcFu46j4Hz94q+76JiQdQWNkv9uO0s4s9o68JQTr2+2YGFu86rfjA5kxuF++dkKp79fhdOp2bLHQrJYPOJNADATzvPyxsImSWoYF7K5ZvSJKtFxQIiZ25Fz2+2yzJ9/KrMSThwtxt+0IK9ihiAXRH597r+jlxW9+BtJjcysuQq55WF+7H/wk289nOCHSIyb+u/1zB7S5JNB7BitU+HcQAXrueix1dbjT720Z/HMPa3Q3aOiNQgNesOzqXn4lRqNnJVfnK31cQ/jmHbv9fw8+4LcodiNyVn9yqNpMnNtm3b8OSTTyIwMBA6nQ6rV682+5y4uDg8/PDDcHNzQ8OGDbFw4cIy28yePRvBwcFwd3dHeHg49u4Vv4lVakv3XkTLj/62uN//ukLGXQxesBdf/H0KsfdaFMpTOpXZcqpsPz+LhCnLh6uP4t9U47N+Fu46j1/3X8blm8oZYFoRJ5LZGqoUMcsT0Wf2ThQWFWPZ3ot4Y+lB/eBhtcnNV+4JX0zztp9Fy0l/SzIeTgySJje5ublo3bo1Zs+ebdH2586dQ+/evdGtWzckJiZizJgxGDZsGP7++2/9NsuXL0dMTAwmTZqEAwcOoHXr1oiKikJamvmTrZKMX3kEt/KLMPqXA3KHYpNhi/eb3eZadl65j6dl30Gn6f9g5sZT+CPxCvr8dwdW3xuETPK4ZcFVd7EI5xydkfuy7zw4KQxfvB9pWdKOQ1t5UJkHZUe08uAVHLqUgX3nb2LcyiNYc+gq/rPyiH7MFSnPJ+vuDuR+d4UyW3MlXVuqZ8+e6Nmzp8Xbz5kzB/Xq1cOXX34JAGjWrBl27NiBr776ClFRUQCAmTNnYvjw4RgyZIj+OevWrcOCBQswbtw48d8E2czcIOnvtpzB1cw7BtM5xyxPRN82taUOTXW2nJQ+eb+acRsJF26a3S47rwDrjySjW5NaqOzqLNrrz/rntP7/Nx1PFW2/9qK2RsiComLcLiiCp3sluUPRKzlDZ0XCZaxIuIzz03vLGBGplaLG3MTHxyMyMtLgvqioKMTH350+nZ+fj4SEBINtnJycEBkZqd/GmLy8PGRlZRnctCInr1CWGQrmpBq56g6p7WXwN8fgWM4e4xiGLjLfGgcAo5YcwOtLDuCDVUdEff3SLX1c10paPb7ahlYfbTTbwgoAfx1Jtsv0cbUPxrXUH4lX8NGfxyQ5dguCgMEL1DdUQ2yKSm5SUlLg5+dncJ+fnx+ysrJw+/ZtpKeno6ioyOg2KSkpJvc7bdo0eHl56W9BQUGSxG9vVzJuo+Wkv9HvR9OJnVzmbitb08a3qqsMkTyw//wNPDFrO/Y7YH0TS5xItizpv1/WYCW7EFXtfpfPzqR0s9uOXHIAA+fvxRd/n5Q6LNX5I/EKdp+1bir6W8sSsXDXeWw4Zvq8Zas7BcXY+q996xgFj1uHpDRljWFTVHIjlfHjxyMzM1N/u3Tpkt1jMDbGAKhYU/afiXdrf+w7b74rQQylrzKs7Q/fcuqaReXSM29Ls1zDs3PicfRKFp6do7xkUOs+33ASoxQ2vszUb9JWSp45IpbZW84YvT85U/6p2HJISsvGW8sS0f/H3TY9X0sFOiNnyr9MR0mKSm78/f2RmmrY156amgpPT09UrlwZvr6+cHZ2NrqNv7+/yf26ubnB09PT4KYUaRY0CQNAxq0C2ddROV7qyr7bjDhcz8nDyJ8TEHfKsjEhC3edN7vNx38esyU8UrDv4s5g3eFkucMwoNOJm95k3HLcNdRKVl4vKhbw4eoj+CNRnJa9X/fZ/2LUUlcy2HWqVIpKbiIiIhAbG2tw36ZNmxAREQEAcHV1RVhYmME2xcXFiI2N1W+jZfN3nJP19Y211Hy6/gT+OpqCl3/aZ9E+SlYSNdVqddDB1/w5cjkTN41c0TlyIcftp9Pxw1bjrQakLOsOJ+Pn3Rfx1rJEi7YvObbK2CK7Y38/LFZoosrJK9T02Ba1j4iUNLnJyclBYmIiEhMTAdyd6p2YmIiLFy8CuNtdNGjQIP32r732Gs6ePYuxY8fi5MmT+O677/Drr7/i7bff1m8TExODuXPnYtGiRThx4gRGjhyJ3Nxc/ewpLVudKG8J+vsr8pZ0+YZtzdF5hUX4nwMVu7LUvvM38OR/d6DDtNgyj70wd48MEdmPuYPptL843qOixO6KMyY9x7LW6PumrD2u//+p6237Ny4qFjD6lwNYZEHLsFjEeC2RGw+pBEmngu/fvx/dunXT/x0TEwMAGDx4MBYuXIjk5GR9ogMA9erVw7p16/D222/jm2++QZ06dTBv3jz9NHAA6NevH65du4aJEyciJSUFoaGh2LBhQ5lBxkpjrjy4JWNvlFjwruSBrKCoGJWcy8+X77+Dn3dfLHc7qSVcuImwutVljcGY+917eYVluyCtPWkohbHvbb6R96c1F6/fQpBPZdG7v8Qm9rIR1hR1u1NQhHVHLO+uNHUIXHckGVv/vYa1h5MxuGOwxfurCEeZ2aVWkiY3Xbt2LfeEbKz6cNeuXXHwYNkWgpJGjx6N0aNHVzQ8uzK3Mmu+BeNpktKMV45Vii83/otxPZuWu8396d9yrwXzzPe7WD/DTlKMlAX4ec8FRDaX94JEypTjp53n8PGa43ipw0P4pG+IhK+kPBdvWF7BWqxyENlGurPIsSlqzA2Vr1CB9WzOlhiHs3yf+dYYS6YoKvs6V1pyjBkXBAEzN/0r2f6NfW0taYVSw0KUpny24W73itwtlI7iwnV1LgmiE/lol5tXiBUJyh2AbU9MbuzEkpaZirqVX4jVB68gU8GzNhTYs4Y7BUXYeCwFuQqYyrvygP2XBNhyKg3fxp42v6GKCYKA06nZql2vSKuOXxWnoKpS1t6zltg9lh+sOoKJf0g72zQt6w76K7C2WmlMbuxklR0Knk1YfQxjlidiyELtjuCXwoTVRzHifwmKWOfL0tIAYkrJVOZYHjET4RUJl/H4V9swwoI10ch+pKg5Ne73wxYvSFwRYiQmN2/li7oQ7RoT5RbEvOAd/NM+7D6r/EKoTG405H5diQMXM2R5/ZsKbjEqz4p7AyCNrVpO2rDgXhmFkv/GCh/naze2DO7OvF2Ao1cyJYim4pbtu4TnVFKo8/MNp9D5sy0WLYFREa0nb8QlK8ZCAYYL2ZZkaSVzuTG5UZldFpRKVz3d3S62V/+3H6u4cjNJROzxDmr1/u9H8K+VNZS6frEFT8zagV1nbD8elVwkU0tsaXG0Rw2zvyVY6kHJmNwowN5zljfx2XvNELks2HEOfx9LxdvLD8kdimy0evBXjHJym2vZefh13yWHme47e0uS/v87N/Q1u/39VtrNx21frd7WpQemrT9h0XZTLdxOCaxNLpVKSeVKmNwowPM/2NaEquVmdbV2cYmp/adlC/kpja2FzBR0DDTquTm7MPb3w5iy7rj5jTWg5ExMF2f7HFj+OWl9YnQl4zZ+MLIorzE/WridrTR8+LXZygPKWUyXyQ0pki0HPlPyCovw33+0PRtILpNEXAdMipPFrNjT6PrFFqtn09xf+XzT8VQzWxqn1OTN1AXRusPJ+F/8+bvbmNmHWJXF406qvBVay1eX93T9YgsSrVgOZ/G975ASMLnRiNLNgWpeh+jstVyDdawq2hU3fuURzNgoXR0XtVNzPRlzvtz0L85fv1WhAZuOUE0ZACZYOIV4wuqjEkdCSnH++i28stCydQOVhsmNDNZbUW68DBMXC6VLnj/+lbzLz4t5UVPRxelMNZWeS891mBNXeeRoZVBCy4YlX9Fr2Xlo/OFfVs80USolfO5akXFLvNo6ShqrUppax50xuZHB60vEr6cyf8c5RbSSDl6wF5m31TFeptuMOFUUo9IiSw7lSjrelx5blJtXiDPXlL0cijFL9pRfMflqxm0Nt+OJa3G8Yyz8q4Tzii2Y3JCotv57zWDmhdLJVROIxJOUlmP3mitdZ8Sh+5dbcfDiTbu+rtS6zoiTOwSqgCIJluhRaW7D5Eat7i+1oETmmmuTM6VbNPP3hMsYvng/buXLv5QCWaf0Yfm4hcXCImduxROzdlg9tdiWK9JjVzPx56Gr+jE8G00MODa2qrsaiN1Nq7Xkj9SDyY3K3C889uHqoziZosxBw1cybmPudtNFqXpIOB7onRWHsOl4Kn7aeV6y1yCJWHDRmXmrAKN/OYAtRmbTpWRKXxeo97c78ObSg5K/jlb833e75A6BKkin0n4pF7kDINusOXRV7hBM2pl0vdzHTZX1FpNaxv2QdVpP3ggAWHs4Geen967QvqypUMxxKCQle3y/bE1S1JnasOWGiIhspJYyAqdTs62qBG+OmAtRKp5KsxsmNxJZeeAyeny1FedL1Gsh+7F1auW7KxxvuQc5Tk9KnvpqjfvHfSkGcpJ4Hv9qG57/IV60Kf2nyqkjptJeHJNKvh2pF/gUE5MbicT8egj/pubgP6uOiLrf8n44jroQoCAImLP1DGJP2FZNtqTS9YK0bldSur4yrdp8tcmwMKNcrQi384vQcbryl8og4MJ1bdQrksuwxfvL30BBmR3H3EjsdoE0BZDuJjK8WgSA3WdvYPpfJ+UOQzVOJGfhg1VH8G5UE7w4b4/c4djsm9jT6N6sls3PN3Yczr5TgN+tTHC3/nsNqVnquaIlskbJc9ghK5ZikBuTG41QWyu/mBVfU7PKzpJR2+dhTy/M3Y2MWwV4ca58iY1YM/0qMnDc2DXm28sPYbMILYCkTPZq3duVlI6/j6VgXM9mqOzqbHZ7JR+vCoosD05JwzDYLaVWRo7M+UXqqa1hqj4ISS/DkQZDliM3vwjdZsRhbonVo00lNocvZ2DlAcfqspSaWgYjX8/Jw7BF+7HZimPWi/P2YFH8BczZekbCyAwpYYkQJc1SZXKjMrpS/yWiu2y5+j2XnotP158wu92+8zcR82vZweZSDDE4cPEmhi3aL9tVMI8thqb/dRKbT6SaH29yT8nvoT0Tjm5mqks72r8ru6UkJlVzY+kKqOWN3ndE6rgmJCrr6XuF7y7fvIUNYx6RORptseV4nKaSGUKFnLFngC03pElK7sMmaVSkFWX32esotvHkINUEEbm6GbT00/nf7gu4U2JArFjvzdJSBistXCInKU19i7AqHVtuNK6oWEDChZtoVccL7pXMD2zTilOplq1LRPJbc+gq0nPkvTru/+NufP5sK1ljUAprx2T9uv+SRJFU3ITVR7Fkt/JX776SId16e/cpaJa2XbDlRmWs/YL+958kPP9DPF77OUGagBSqoFBL15/a9sbSg/h4zXFk58m72Om6w8myvr4x+YXFdk/8Eq2c7jv2t8M2vY696nKVnJlnS/FIY8/462iKVdtbqqCoGD/vvoAz19TbkhP99TZcl/liBWByo3mL7xVoizt1Td5AJKSWWRdKsGSP8q9irVHyXPXkrB04YeFK4mrx8JRNaPvJZkVNsS2pIt2/av7dLtx13uRjF67b9m8lCAIW7TqPD1cfRfcvt9oYmWl3CopRaIcZtSdTsjHrnyTJX8ccJjekTRptgi19RVRQVIyhC/dh4Pw9GL/yCHLMtH58sOqolOHJqlgARvzv7oyWzFsFeG6OfVakluqrlptfpP/3LK+lgJTFXLeeqZa41Kw8JFy4KUVIAIDPNpxEty/jJNt/SXmF0hSvtQaTG6JSus2IU+waKj2/2W7w94ajKYg9mYbtp9OxdO/FMksSOJpLN27j/d8O479bTmPfeetOFEoek1CswRHyciwXY49Pceu/5beS9yr1G7anSzekH9ujFExuVMZR14+yp3PpuZj1z2m5wzCq9LTU0st7nL2Wg6f+uwPjfrdtHITaGEtIlu+/hLnbz9nn9fl7VBcF5IhqmVqudkxuSJPyCyvWt6zWmhHbT6fj8OVMLNun3Bks5Lhu5xepbkX4kvGqLXYx3ZFonUSp2CW5mT17NoKDg+Hu7o7w8HDs3bvX5LZdu3aFTqcrc+vdu7d+m5dffrnM49HR0fZ4K1YTAGw8Jl5/ubF1lKgsa2d8aIUWuy/sxZY2mOTMO/xNWuh0ajaaTdyAt5cnyhZDRZeoqTd+vaSz6pT88+00/R+5Q7CK5MnN8uXLERMTg0mTJuHAgQNo3bo1oqKikJaWZnT7lStXIjk5WX87evQonJ2d8dxzzxlsFx0dbbDd0qVLpX4rNjl0KQMj/ifeNOwVVq5YTNpW+oSs4GOj4tny2f1+4DIm/HFM9FhKU/J4IEvN3X53Da/ViVdli0GMYnmjfjkgQiRlKX322PXcfLlDsIrkyc3MmTMxfPhwDBkyBM2bN8ecOXPg4eGBBQsWGN3ex8cH/v7++tumTZvg4eFRJrlxc3Mz2K569epSvxVNyJW5lgiJ505BERcgFZGSyyUo+YqexJPCVkDRSJrc5OfnIyEhAZGRkQ9e0MkJkZGRiI+Pt2gf8+fPR//+/VGlShWD++Pi4lCrVi00adIEI0eOxPXr103uIy8vD1lZWQY3RzR7SxJaTPpbkcXKzDmXnouvN/+LW/n2Sc6UfqF8MzcffWfvxKZSyY3aToK7z5r+3VoiN09d4wBIuQqLivH0dzvx3grDBVIv37TPDCMddA7bnS4FSZOb9PR0FBUVwc/Pz+B+Pz8/pKSYH4eyd+9eHD16FMOGDTO4Pzo6GosXL0ZsbCw+++wzbN26FT179kRRkfED3bRp0+Dl5aW/BQUF2f6mVKT04Lcv/j4FAKqcSdNtRhy+3nza6Mq3UpzQl+y5iC0njXedyq24WEDnz/4xqLyqRufSc9H/x90V2ocS6mnYQ/YdZba42lqXZc2hq9h7/obI0VTM3vM3cOBihkHX/5WM2zin0AKKSqaEiyxFz5aaP38+QkJC0L59e4P7+/fvj6eeegohISHo27cv1q5di3379iEuLs7ofsaPH4/MzEz97dIldc8k6Tgt1qLt2n0aq7p+UnNSs+w3jXLIwn12ey1rRM7citx84yd1NY3N4GKBlsu+Y916T/aSZWNcbyw9KHIkFWfshDxw/h77B0KikDS58fX1hbOzM1JTDZvOU1NT4e/vX+5zc3NzsWzZMgwdOtTs69SvXx++vr5ISjJe8tnNzQ2enp4GNzW7mmlZv6zcixGSNM5aeCV5VsXr05A6WHOFrsaaQGevqaPV5tjVTLlDUBxJkxtXV1eEhYUhNvZBS0NxcTFiY2MRERFR7nNXrFiBvLw8vPTSS2Zf5/Lly7h+/ToCAgIqHLMY1LzoGWnHY19uxT6FNf2TfA5JMJ7D0pabKxm3sVzBq4cD8nelVGS2lBJbwuQmebdUTEwM5s6di0WLFuHEiRMYOXIkcnNzMWTIEADAoEGDMH78+DLPmz9/Pvr27YsaNWoY3J+Tk4P33nsPu3fvxvnz5xEbG4s+ffqgYcOGiIqKkvrtWOS5OZYNliYSW+kDtJIHj9tjET96YM0h8adgW1osc8wydZ18C4uKkXFL3i795fsuWrxtXgF/S6W5SP0C/fr1w7Vr1zBx4kSkpKQgNDQUGzZs0A8yvnjxIpycDHOsU6dOYceOHdi4cWOZ/Tk7O+Pw4cNYtGgRMjIyEBgYiB49emDKlClwc3OT+u1Y5IZKx7lk3i7AgQs30aWRL1ycFT0ci2ygtMG3v+y1/OBN6nbh+i25QyhXYVExfkt40LLU97udOHrFvrNqS3fbvf/7ETze3B8+VVztGodWSJ7cAMDo0aMxevRoo48ZGwTcpEkTk2WuK1eujL///lvM8OieF37cjePJWXg7sjHeimwkdzgVVlwsYPe5ik011orESxnoO3un3GEYEGM8w20TA6u1RgGTTypEqQPdC4qKUcnZCUv3XjQoLmjvxMaU0mvHkeV4ee6ATB0ojyff/UH/kXjFfsFIaFH8ebw417FnOyzcdR5/HUnG9L9OyB2KJMatPCJ3CA5NoTmLRX5PuIxGH/yF9UeSseecesamxZ+5js0s3mkWkxtSPVMDAVce0EaSVlEjlxyQddaHqROgIy9CqBU6C5tklDZTavm+i3jnXrG+15dIs5yCtYwNKNbhbgv0ieQsFN1bzPeFubsxbPF+rmlmBpMbIgeQls2yAI5u3o5zou9TWSmL5d7/3bDFz9IkTUqmlsb5YuMp9PxmOz5eY7iGmZLrRCnhuoXJDZWhgO+lVW6xX1qVlHBCsYf7V9wVoYSTRUUo5Z9aKXEYY6xat04HfB93BgCwOP6CwWPj2SVbLiY3DihHY4tnnlNJoS0y5CjdUh+sEuMkpO7PKtnCwqNSM/WVU0LOk55TdpZted15F288mIF2JcM+61+pCZMbKuMqfyhkhQ1HkxE+dbPJgoFKvlq2h2X7lF28jpTL3G8nPScPo35RxpghpWFyQ2XkWViYiwgAXvv5AFKz8jDkJ2WuxaUFxfxJSkqtg3M/+vOYIgt1VqTasliY3BCRKAqtPAOnqPSEIoc/bawuvPrgFXSYGosjl7n2UHmUOhXcXKOnUrujdp2Rv74YkxsHdiM33+S4h23/XsPY3w6pYnyOqaZbJVw9kGkijLN1GLYWcxuzPBEpWXcweqlEXRcO3uUot4MXM2x63qUbtyRdXiL7jvznDSY3Dir2RCoenrIJ4343Pthx0IK9+HX/ZcyKPW3nyEitlFbLhB4oLGImCcgz/utkShau59hYiqFUvDM3/VvheNKy7qDL51sQOnlThfelZExuHNSUtccBwOxKvUpt9rSEGJNxVh9kIUAiU9SWzsoxQS/66+0I+2SzTc8tPRP0WxEuNo9elb6LMvO2ZavFS8kua0uR8pxX+EJ2SjFmeSIeaVyTi9cRacCMjadUVYKgSEWxKg1bbggAcOhShtwhKFb41M1I4+BXs7jIHxnz4WrlFJsTBGDGxop37diNBLmNo3QfM7khAEAfha0YrSQFRQLeXHZQ7jCIFMeSKtM/775oh0jIUtdsHf+jMkxuiCxwmFNpLTJn6xm5QyA7cvQCjVKTolNq7G+HrX7O7Xz1tcoyuSFsOZlmdhslf7lNrzpt1zAIwPS/TsodApEipShkCQpbDFqwR+4QrMbkhjBkofnKsjM3nbJDJMrFRImorGIWK7KYtUUulWTf+Ztyh2A1JjdUrvt96rvPKrOCJxHJJ0sBxdq0jBdVtmNyQ+XKU8EMGP7+yRHcyJWuoixJz5ZEhVXWbcfkhsq18XgqAHX+yIpFvOzhNGeS28NTNuFatmPMdCH7uJWv3ZY3JjekeqYGFJ9MybZrHES2upadZ1FxuYQLtnUPc1aT/Gy51vo+TtrZh1duqrcCvTlMboiIZPTrvkto9+lmTONMM02zpfVbCatrqxWTG7IIB7YRSeP+Om8/bjtrwdZsglErJR5Dtdyix+SGiIjIAVlSYdpWci+eyeSGiEglvo09jbxCDm5XIwU23EjaDvjeikMS7t08JjdkESU2qd6n5aZVopKOJ2dh/o5zcodBNlDiauRSttz8Y0HleykxuSHVU+Axg8hyVp5fTqfmSBMHSUqJhyktXxcyuSEiIpJYYZHy0hspW70LZV6ag8kNWURJP8vTqdn4v+92Yuu/1+QOhcgiN1ld2OH9lnBJ7hDK0Gm47YbJDanOwPl7cfBiBgYv2Ct3KEQWsWRxWkulZat3dWlHlqbA6tJaHq/oIncARNZKyeLBndQl8VJGmfsEQcCbyxKRXWLxyaQ081W1dyaxsBuJQ8vJjV1abmbPno3g4GC4u7sjPDwce/eavuJeuHAhdDqdwc3d3d1gG0EQMHHiRAQEBKBy5cqIjIzE6dOnpX4bDk2JI/3v0/IPlLQrJesO1hy6anBf5MxtMkVDUlPiIVTK2VIAkH1Hvlo3kic3y5cvR0xMDCZNmoQDBw6gdevWiIqKQlqa6Wlinp6eSE5O1t8uXLhg8Pjnn3+Ob7/9FnPmzMGePXtQpUoVREVF4c4dXtETkTrYc7wlLwDkJ+ZCvmoxYfVR2V5b8uRm5syZGD58OIYMGYLmzZtjzpw58PDwwIIFC0w+R6fTwd/fX3/z8/PTPyYIAr7++mt8+OGH6NOnD1q1aoXFixfj6tWrWL16tdRvx2E54O+SSBUEQcDhyxmyXiWTOkmd8247nS7xK5gmaXKTn5+PhIQEREZGPnhBJydERkYiPj7e5PNycnJQt25dBAUFoU+fPjh27Jj+sXPnziElJcVgn15eXggPDze5z7y8PGRlZRncyDqnUrnCNpGYxDqxbDyeiqf+uxO9vt1ucpurGWzVlpsSrw/PXsuVOwTJSJrcpKeno6ioyKDlBQD8/PyQkpJi9DlNmjTBggUL8Mcff+Dnn39GcXExOnbsiMuXLwOA/nnW7HPatGnw8vLS34KCgir61sgB7T13Q9L9p+cobzaFGFIyeWKV0v1xO5du3Mblm7eMblMkc80RUua4xVQNT85Q3FTwiIgIDBo0CKGhoXj00UexcuVK1KxZEz/88IPN+xw/fjwyMzP1t0uXlFdvgJTv+R9MtzZWhCAI+HHbGbT9ZLMk+5fbxD+Omd+IbFbylPnm0oOyxUHlU2BugzPXtFvtWtLkxtfXF87OzkhNTTW4PzU1Ff7+/hbto1KlSmjTpg2SkpIAQP88a/bp5uYGT09PgxuRUsSduoap60/KHYZoPl133ODvawqs76EEUgzyvXDdeMsNkTHfxZ2RdP8FRcWS7r88kiY3rq6uCAsLQ2xsrP6+4uJixMbGIiIiwqJ9FBUV4ciRIwgICAAA1KtXD/7+/gb7zMrKwp49eyzeJ5GSmOpKUKu528/heokuNkGRow00RDD6v6QwSmy5kVrJGk72Jnm3VExMDObOnYtFixbhxIkTGDlyJHJzczFkyBAAwKBBgzB+/Hj99pMnT8bGjRtx9uxZHDhwAC+99BIuXLiAYcOGAbg7k2rMmDH45JNP8Oeff+LIkSMYNGgQAgMD0bdvX6nfDimQ1LUayHpyryujdT9sPaMfw7H99INlSJQ4roPuYpJvX5JXKO7Xrx+uXbuGiRMnIiUlBaGhodiwYYN+QPDFixfh5PQgx7p58yaGDx+OlJQUVK9eHWFhYdi1axeaN2+u32bs2LHIzc3FiBEjkJGRgc6dO2PDhg1liv0Rkfx4vjWuIuv6TPvrJEJqe6FjQ19klbg65ketXPwd2Jddll8YPXo0Ro8ebfSxuLg4g7+/+uorfPXVV+XuT6fTYfLkyZg8ebJYIZKKqb7dRoMtT5dv3oKf592LDR7T73p+Tjx6hfjj5U71RNlfMmehqQp/B/aluNlSRI5Ge6kNMH/HOf3/84r1rr3nb+CjNcfNb1gBggDM234WCRekLVtA1uPvwL64cCapnhqOGcXFApyctJjGkK2+3HhK9H1m3i7AJ+tOAADOT+8t+v6pItRwpNIOJjekaq/9LwHVq7jKHYZRxcUC8ouK8dayg/j7WCq8PSrhvagmGBBe12C7D2Vcf0UqvEo1TRAE6HQ6rEi4XLH9mHn8nV8PVWj/RGrG5IZUbcMx41WpleCp2TtwMjlbP3Mo41YBPlh1tExyo0WGyQ0znZKGLdqP+S+3k/x1fj9QseSJxMWE37445kZk/9t9wfxGpHk3c/Nx9EoWp0RTGbEn0+QOgWSg1FXB7xQUAbjboph5SzuLrzK5EZmcS7yTMqzYfwltpmySOwxZ3T+QT/vrBNJz8mWORpsEQcDt/CK5wyALKTO1AV5ZuA8A8Mm6E2g9eaPM0YiHyQ2RyCb8UX6Ce/hyBqK+2oat/14rdzs12346HefTc/HD1rNyh6Jp/X+UZr0zEp9CG26w68x1AIYzHLWAyQ2RyMwdxAYt2ItTqdkYvGCvfQKSwe2CItwuYKuCKdtESmwPXc4UZT8kPYXmNprF5IZIRMXFAvIKy18sLkND/dpkm0EaTmzJOCUvjfH5Bu0s3HsfkxsiEVnb1fTPyVTzGxEZodxTJamN1KuDy4HJjYj2nL0udwgksWIzs58ybls3ePaVhfsrEg45MmY3RCYxuRFRjEaLZt3M5WyX+5RcV0dpZm9JkjsETcvOKzS/ESmGIICz2+yIyQ2Z5ejTmktKy+JihZZaezhZ7hA0rai4/LFdpCx3Coow+pcDcofhMJjciOhKxm25QyA72JmUjlFLDuBadh4Aw6sxBY8ZJCIZ7b9wkwUc7YjLLxBZacC8PQCAW/mFeL5tEEYuuXs19vvIjvqEh0hquXns4iAyhckNkRVKLqew5dQ1xJcYRP7M97vkCIkc1Dexp+UOgUix2C1FZIVP1p0w+PtOAcc9EBEpDZMbIiIi0hQmN0RERKQpTG6IiIhIU5jcEBERkaYwuSEiIiJNYXJDREREmsLkhoiIiDSFyQ0RERFpCpMbIiIi0hQmN0RERKQpTG6IiIhIU5jcEBERkaYwuSEiIiJNYXJDREREmmKX5Gb27NkIDg6Gu7s7wsPDsXfvXpPbzp07F126dEH16tVRvXp1REZGltn+5Zdfhk6nM7hFR0dL/TaIiIhIBSRPbpYvX46YmBhMmjQJBw4cQOvWrREVFYW0tDSj28fFxeGFF17Ali1bEB8fj6CgIPTo0QNXrlwx2C46OhrJycn629KlS6V+K0RERKQCkic3M2fOxPDhwzFkyBA0b94cc+bMgYeHBxYsWGB0+yVLluD1119HaGgomjZtinnz5qG4uBixsbEG27m5ucHf319/q169utRvhYiIiFRA0uQmPz8fCQkJiIyMfPCCTk6IjIxEfHy8Rfu4desWCgoK4OPjY3B/XFwcatWqhSZNmmDkyJG4fv26yX3k5eUhKyvL4EZERETaJGlyk56ejqKiIvj5+Rnc7+fnh5SUFIv28f777yMwMNAgQYqOjsbixYsRGxuLzz77DFu3bkXPnj1RVFRkdB/Tpk2Dl5eX/hYUFGT7myIiIiJFc5E7gPJMnz4dy5YtQ1xcHNzd3fX39+/fX///ISEhaNWqFRo0aIC4uDh07969zH7Gjx+PmJgY/d9ZWVlMcIiIiDRK0pYbX19fODs7IzU11eD+1NRU+Pv7l/vcGTNmYPr06di4cSNatWpV7rb169eHr68vkpKSjD7u5uYGT09PgxsRERFpk6TJjaurK8LCwgwGA98fHBwREWHyeZ9//jmmTJmCDRs2oG3btmZf5/Lly7h+/ToCAgJEiZuIiIjUS/LZUjExMZg7dy4WLVqEEydOYOTIkcjNzcWQIUMAAIMGDcL48eP123/22WeYMGECFixYgODgYKSkpCAlJQU5OTkAgJycHLz33nvYvXs3zp8/j9jYWPTp0wcNGzZEVFSU1G+HiIiIFE7yMTf9+vXDtWvXMHHiRKSkpCA0NBQbNmzQDzK+ePEinJwe5Fjff/898vPz8eyzzxrsZ9KkSfjoo4/g7OyMw4cPY9GiRcjIyEBgYCB69OiBKVOmwM3NTeq3Q0RERAqnEwRBkDsIe8vKyoKXlxcyMzNFHX8TPG6daPsiIiJSu/PTe4u6P0vP31xbioiIiDSFyQ0RERFpCpMbIiIi0hQmN0RERKQpTG6IiIhIU5jcEBERkaYwuSEiIiJNYXJDREREmsLkhoiIiDSFyQ0RERFpCpMbIiIi0hQmN0RERKQpTG6IiIhIU5jcEBERkaYwuSEiIiJNYXJDREREmsLkhoiIiDSFyQ0RERFpCpMbIiIi0hQmN0RERKQpTG6IiIhIU5jcEBERkaYwuSEiIiJNYXJDREREmsLkhoiIiDSFyQ0RERFpCpMbIiIi0hQmN0RERKQpTG6IiIhIU5jcEBERkaYwuSEiIiJNsUtyM3v2bAQHB8Pd3R3h4eHYu3dvuduvWLECTZs2hbu7O0JCQrB+/XqDxwVBwMSJExEQEIDKlSsjMjISp0+flvItEBERkUpIntwsX74cMTExmDRpEg4cOIDWrVsjKioKaWlpRrfftWsXXnjhBQwdOhQHDx5E37590bdvXxw9elS/zeeff45vv/0Wc+bMwZ49e1ClShVERUXhzp07Ur8dIiIiUjidIAiClC8QHh6Odu3a4b///S8AoLi4GEFBQXjjjTcwbty4Mtv369cPubm5WLt2rf6+Dh06IDQ0FHPmzIEgCAgMDMQ777yDd999FwCQmZkJPz8/LFy4EP379zcbU1ZWFry8vJCZmQlPT0+R3ikQPG6daPsiIiJSu/PTe4u6P0vP35K23OTn5yMhIQGRkZEPXtDJCZGRkYiPjzf6nPj4eIPtASAqKkq//blz55CSkmKwjZeXF8LDw03uMy8vD1lZWQY3IiIi0iZJk5v09HQUFRXBz8/P4H4/Pz+kpKQYfU5KSkq529//rzX7nDZtGry8vPS3oKAgm94PERERKZ9DzJYaP348MjMz9bdLly7JHRIRERFJRNLkxtfXF87OzkhNTTW4PzU1Ff7+/kaf4+/vX+729/9rzT7d3Nzg6elpcCMiIiJtkjS5cXV1RVhYGGJjY/X3FRcXIzY2FhEREUafExERYbA9AGzatEm/fb169eDv72+wTVZWFvbs2WNyn0REROQ4XKR+gZiYGAwePBht27ZF+/bt8fXXXyM3NxdDhgwBAAwaNAi1a9fGtGnTAABvvfUWHn30UXz55Zfo3bs3li1bhv379+PHH38EAOh0OowZMwaffPIJGjVqhHr16mHChAkIDAxE3759pX47REREpHCSJzf9+vXDtWvXMHHiRKSkpCA0NBQbNmzQDwi+ePEinJweNCB17NgRv/zyCz788EP85z//QaNGjbB69Wq0bNlSv83YsWORm5uLESNGICMjA507d8aGDRvg7u4u9dshIiIiC/RsaXyoiD1IXudGiVjnhoiISFoLh7RD1ya1RN2nIurcEBERkWPS6XSyvTaTGyIiIhKdk3y5DZMbIiIiEp8ObLkhIiIiDZGxV4rJDREREWkLkxsiIiISHVtuiIiISFM45oaIiIg0hS03REREpCky5jZMboiIiEh81dwryfbaTG5EtDnmEblDcCjV3CVfGs1ir3dtIHcIRESK0sivqmyvzeRGRA1rVZM7BIfiVVm+q4LSxkY3lTsEIiK6h8kNqZaTnKPVAHRu6AsAiKhfQ9Y4iIjIkHLa9YmsJHNug9kvPoy1R66id0hAhfc1KKIuzqXnYvvpdBEiIyKSHwcUa8iYyEZyh+AwgmtUkfX1vTwqYUB4XXh7uFZ4Xx892ULWFXSJiLSEyY3I5Cxa5Gia+ss3xunvMeIOHndy4jeHiEgsTG5ExotvO5Lxs25iJLHyVNDsLSIiucnZGs3kRmTMbexHaW0dzk4Vi4eJMRGROJjciKx6lYqPvyDLKC0Z4JgZkkutam5yh0CkKExuRNaB04LtqlEt+YpEic3NhT9Hss1LHerKHQKRovBoSqrVN7Q2Nox5BO2DfeQOxWbV3F1wcko0AGBMZGOZoyG1qmiXKBEA1KleWdT9cSq4hrBnwn6qe1SCs5NOMZ95yTAim9Uyu31IbS9sebcr3Cs5A1BWxWUicjzTn24ldwiiYXJD6nUvmzh4MUPWMO4rmWRZ0gozsmsD+FbV5liJHe93w9f9QuUOg4isoJQLRTEwuSHVuj9bKsDb3a6vu/aNzma3saQVpoqbdqeO16nugSdbB+L5tnXkDoWILCR2biNnssTkhlTvyVaBdnutVx+pj5a1vUw8avkv+YX2Qehyb20qrXJ20uHzZ1vLHYbi/T4yQu4QiAAAPlW1M9uXyQ2pnr2uDkZ3a1ju6t81S0zHNRfTtKdbwYmDQAlAWF0fPN2mttxhEKGpv6fcIYiGyQ2RhULqeJU7K2X2i20QXs8H/xvavtz9/PVWF7FDI5WrW8F10rQ0VoLk8ceoTqLvU87aX9rt9JeJIMgdgeNRymdev2ZVLH/1bhfDlYzbRrfx93RHswDtXB2RvNrWrY5RjzXEyeRsuUMhlWsd5C13CKJickOqZe+LAmuSKI9707tL4xU2iem3kR0BgMkNUSlMbogkUL2KKz57JgRJaTlwc3HGf7ckAeDaY2Qck14icTG5IZJIv3YP6f9fn9zwLOawantXNtldKYWGtaoiKS3Hbq9HpCSSDSi+ceMGBgwYAE9PT3h7e2Po0KHIyTH9Q7tx4wbeeOMNNGnSBJUrV8ZDDz2EN998E5mZmQbb6XS6Mrdly5ZJ9TasxnOX/QlQyKAbonJ88eyD6q9N/auhRaAnPn6qhSj7NnbcceFsPMWZ+n8hcofgMCRruRkwYACSk5OxadMmFBQUYMiQIRgxYgR++eUXo9tfvXoVV69exYwZM9C8eXNcuHABr732Gq5evYrffvvNYNuffvoJ0dHR+r+9vb2lehtWU8rgVkdQzd2+DY+uLjxZkO2cnHT49oU2+HHbGXz3YhgequGhf6yi3ywed9Shb5tA/GfVEbnDcAiSnB1OnDiBDRs2YN++fWjbti0AYNasWejVqxdmzJiBwMCyRddatmyJ33//Xf93gwYN8Omnn+Kll15CYWEhXFwehOrt7Q1/f38pQieVaFXHC24udwft2uvAXs294ms/NQuoJkIk6jLikfr4cdtZucOQXbtgHzg76fBU64oXnRzepR66NTG/fhmRo5KkWyo+Ph7e3t76xAYAIiMj4eTkhD179li8n8zMTHh6ehokNgAwatQo+Pr6on379liwYAEEM2e3vLw8ZGVlGdzIctOeVl5Tamc7V/cd2KEu2tatbvPz17/ZBYMi6mL6M6YXpvPztO8yEvbyRKsAuUNQBDFX7v6gd3N0LPEbYHe4Org6s7ScvUjScpOSkoJatQyvKlxcXODj44OUlBSL9pGeno4pU6ZgxIgRBvdPnjwZjz32GDw8PLBx40a8/vrryMnJwZtvvmlyX9OmTcPHH39s/Ruxifbah32ruqFRrao4raDBia4u9j1ITHiieYUGAzcP9MTkPi3L3UbMk5+S6DhHjAgA4MLkxm6s+qTHjRtndEBvydvJkycrHFRWVhZ69+6N5s2b46OPPjJ4bMKECejUqRPatGmD999/H2PHjsUXX3xR7v7Gjx+PzMxM/e3SpUsVjtHRKG2Rx6b+jte9Q+plbnBvRVte+HsgMmTVGeudd97Byy+/XO429evXh7+/P9LS0gzuLywsxI0bN8yOlcnOzkZ0dDSqVauGVatWoVKl8sc5hIeHY8qUKcjLy4Obm5vRbdzc3Ew+RuYJgmCwbpLcWgd5I6rFg++R0hIvotI6SdyN2i7YR9L9E6mNVWeFmjVrombNmma3i4iIQEZGBhISEhAWFgYA+Oeff1BcXIzw8HCTz8vKykJUVBTc3Nzw559/wt3d/BiExMREVK9encmLA+nfLsigi6hD/RqSvybHNFBFBHpXlnT/TPCJDEnSAdisWTNER0dj+PDh2Lt3L3bu3InRo0ejf//++plSV65cQdOmTbF3714AdxObHj16IDc3F/Pnz0dWVhZSUlKQkpKCoqIiAMCaNWswb948HD16FElJSfj+++8xdepUvPHGG1K8DbpHaaOISrfwa3SoCjmQF8ProrpHJfRvFyTaPlkwkiy1ZnRnuUMQnWTp/pIlSzB69Gh0794dTk5OeOaZZ/Dtt9/qHy8oKMCpU6dw69YtAMCBAwf0M6kaNmxosK9z584hODgYlSpVwuzZs/H2229DEAQ0bNgQM2fOxPDhw6V6G1ar7uEqdwhEpDDtgsufaedTxRX7P3wczk46LNsnzpjAL59rjV7fbhdlX6RtIXW85A5BdJIlNz4+PiYL9gFAcHCwwRTurl27mp3SHR0dbVC8T4lqVNVe91ht78osEkY2YwOCZWNixJwtt3R4BzQP5Orz5Lg4L43K9VW/1mhZ2wtK6pySY2qxM8/QVAH2vjiIaCD9ODQiJWNyQ+X6vzZ15A6hrFJ5hj3GFjhxYA8RkWowuSHVYZpBjuynIe2M3j8ooi4AYHPMI/YMhyzwVvdG+Hmo6ZnCJD7OHyQikphbJfGuI02tKTW5T0uzVbBJHm8/3ljuEBwOW24kUN2j4gssEpF2aHXdMCKlYnIjAS3Wl1DSbCktfr5ERCQeJjcSMDelnciRMBclIntjckMmTenTQu4QjOK5Ul28KrOblojsi8kNmTQwIljuECzCZEfZXJx4mCEi++JRRwIcEyItnivVhT8H+fwynNOPyTHxNCEBHsulJUeFYiI16tjAV+4QiGTB5EYCWhxOrMX3RFRR56f3ljsEIjKCyQ2pDrs5iIioPExuSHUsWWGZlEPruegXz7aSOwQiKoXJDalOoHdlg79Z/ZXk9FzbILlDIKJSmNyQqrzZvVGZ+/y9mNwoSc1qbnKHQCW4i7iuFZFa8FtPFqnqpow1VmO4AJ36aL1fSuGWDu+Apv7V5A6DyK6Y3JBFerTwkzsEIrJBm4eqY8OYR+QOgxTKSaMXH0xuJKDF74ozpyiRGe9FNQHAAbZEJD9l9DUQKdg/7zwqdwiqMKxLPQzrUg9uLs4G97PoIpFyabWGGVtuyKinWgfKHYJi1K9ZVe4QVKN0YkPK8UnflnKHQGQ3TG4koIVMuLEfT+hkHbbQSKeJX8UHBL/Uoa4IkZDWvND+IblDkASTGzKqc6Oacocgq2oKmR1GBACuLjxUkzS0OpOOvxgJqP36NebxxggN8ja4r81D1eUJRias1WI9U2POORadSLkELXQ1GMHkhspoHuBZ5j5HK5T3w8AwhNfzwS/DwuUOhYiIrMTkhsrQaCJvlYa1qmL5qxHo2NBX7lBUw1QDjZsDdKn4eUrb0hfsW0XS/ZPjEjTadKP9o44MtPlVISqfzkT/UzX3SnaORHs+fqoFnm9bB7+PjJA7FNKY0uerX1/VxneMyQ0RVdgPA8PgrNBSpxH1a8gdQoX5VHHF58+2RlhdH7lDIY0Lqe0ldwiiYHJDRBUW1cJf7hCMCvRyx9IRHeQOg0ixNNorxeRGCsq8frWc2uMXg6kuFiJrVXFlYUMie2NyIwG1nxc1msgTSeaRcupCrXmjsx0jIbJO6eO92s9f90mW3Ny4cQMDBgyAp6cnvL29MXToUOTk5JT7nK5du0Kn0xncXnvtNYNtLl68iN69e8PDwwO1atXCe++9h8LCQqnehk3asl+cSBGGdKpnl9fpX06VVy7fQUqm1dlSkpVhHTBgAJKTk7Fp0yYUFBRgyJAhGDFiBH755Zdynzd8+HBMnjxZ/7eHh4f+/4uKitC7d2/4+/tj165dSE5OxqBBg1CpUiVMnTpVqrdiNamnhUpNI4k7ObADEx7H1YzbaBFYtmaTFEwNpu7MUgJEspCk5ebEiRPYsGED5s2bh/DwcHTu3BmzZs3CsmXLcPXq1XKf6+HhAX9/f/3N0/PBwWnjxo04fvw4fv75Z4SGhqJnz56YMmUKZs+ejfz8fCneCmlE/3ZBcodAduRTxRUta3vJPnbqx0Fhsr4+kaOSJLmJj4+Ht7c32rZtq78vMjISTk5O2LNnT7nPXbJkCXx9fdGyZUuMHz8et27dMthvSEgI/Pz89PdFRUUhKysLx44dM7nPvLw8ZGVlGdzINC02Uk5/ppXcIZCdiLHIpLVMpVAerlyjjJStdK+UVopuSvLLS0lJQa1atQxfyMUFPj4+SElJMfm8F198EXXr1kVgYCAOHz6M999/H6dOncLKlSv1+y2Z2ADQ/13efqdNm4aPP/7Y1rdjNbmvFomIiGyhlfOXVcnNuHHj8Nlnn5W7zYkTJ2wOZsSIEfr/DwkJQUBAALp3744zZ86gQYMGNu93/PjxiImJ0f+dlZWFoCB2UxARkXWqubsg+46yJrFUhKDJtnork5t33nkHL7/8crnb1K9fH/7+/khLSzO4v7CwEDdu3IC/v+XFvsLD7y5amJSUhAYNGsDf3x979+412CY1NRUAyt2vm5sb3NzUPchXTcZENsLXm0/LHYZev7ZMZIlIHNvHdkPo5E1yh0FmWJXc1KxZEzVrmq7ncF9ERAQyMjKQkJCAsLC7A+r++ecfFBcX6xMWSyQmJgIAAgIC9Pv99NNPkZaWpu/22rRpEzw9PdG8eXNr3oqkNNKqpwljo5vgtUdsb/UjItNa1/HCocuZcodhN5WcdfD2cJU7DFFp7f3cJ8nIoWbNmiE6OhrDhw/H3r17sXPnTowePRr9+/dHYGAgAODKlSto2rSpviXmzJkzmDJlChISEnD+/Hn8+eefGDRoEB555BG0anV3MGiPHj3QvHlzDBw4EIcOHcLff/+NDz/8EKNGjWLLDBlV37cqnBS65hGR2n34hHIuKu1Ba4vAPhdWB0+3qS13GJKQbFj0kiVL0LRpU3Tv3h29evVC586d8eOPP+ofLygowKlTp/SzoVxdXbF582b06NEDTZs2xTvvvINnnnkGa9as0T/H2dkZa9euhbOzMyIiIvDSSy9h0KBBBnVxqOKYCpAa3e9+HBPZSOZIHIejHSsWDmkndwii+uK51nBx1sbsqNIkm6fo4+NTbsG+4OBgg8qIQUFB2Lp1q9n91q1bF+vXrxclRqlotOCjKrGL0HFMfyYE70Y1Qc1qbMW1F0f7fbWq4y13CGQhbaZsRPc42LHXoel0OskTmxfDTS+zQCSF7WO7yR2CKjG5kYCjXc2UxpYr0qonWgUYvV/K37xXZW2N8yDrBPl4mN+IymByI4NaCm82Z25CpBzPhdWROwRVa1XHS+4QHNIXz8pbFZ7JjQR0Dt4Z4ugtV0Ri4sVGxWhlOQG1eU7m+mL8V5eAuYqPSj9YVTQ3YbeU8pycEi13CGQjJf+eGtay/zpe1lLy5wcArYO85Q5Bk5jcSCCymZ/5jcgutLJOSkW5V3KWOwTSII4HMs/ZTJ2tRxr52ikSx8LkRgKVXdV9IlH4hY5VmNoQkRjC6/nY9LzolpYvOUTiYXIjAaU3g0rNwd8+kWK1CPSUOwS7E+t41N7G5IbkweSGyqhRtYJrjUiU3f0+sqMk+yVSMjFXbW4XzBO0Gj39sDxLJNT3rSLL64qByQ2VUce7stwhGBVWt7qk+3evpM2fQ6CXu9whSG7FaxFyhyAZR28J1jpLus6n/l+I5HEY46bisXraPJoT2aBjAw7sUyu5WyQcvfyDkmnhX0aKCQHdm9Yyu42g4syayY0kzEwFV+/3RXU4WYrIsflWFadoqtaO2x891ULuECTF5IbEJ0NGUdXN+Bqw1oTCPEidejRn6QW1kGNAMy9wympQs4rml3VgckNlVPgCRYZLnM+ekbfUN8mnmrs8tVZ+fVW743yssXR4B4u2a+pfDX+O7oz2HNRsSIbsy9L6X6EqLjDI5IY0oVeIP2Y81xo9WVMCgGPNLBNzNpE1ODX4rogGNSzarm+b2nB20qGWp7LX1rM7Cy8GI5uZHyNj+UuW/5qb3n4Eb3ZvhA96NxPtNe2NyQ1pgk6nw7NhddAswPHqeJQ24pH6BjPLWKVZHjUVvkAuWcezsvGub3up7lHBEh1WaORXDTGPN5atVVQMTG4koPaBZxWNX863X5HTeOS9sRvV3OU9iInli2dbwaeKK2YPeFjuUBzS1ve6irKfPqGBouyHbOfq4oRBEcFyh0FW0MZRnMro1zYIy/dfkjsM2VkzRbdXSADq1vBAEz/lLwZYnvvv+Lm2QXg2rI6+5cbFSYfCYuVm3g1rVUVSWo7Vz+tnwerDPwwMw6v/S7AlLItUcin7PfNwrdjhdd6gtmge6IlAhdadciQ/vBQm3fpsFrasKveXq0xsuZGAEr6Enz3rmANsvTxsb0bV6e7Wuqkh0tRRqa14LQJjIhth2tMhePOxhg8eKHGsLNkl9ZTCWwBsaXXbNe4xhNc3P+YjqoW0Y7GkSIgruThpMrEZ3qW+3CGoxquPPvisxOwRcISuaiY3slBC+qNNwTVKlQs38Rt++CFvyWOxxciuDSzetl2wD8ZENsYL7R9CTI8mZrfXYqE5pZz8HeFkUVH3P6HWQd44NKkHXF0c5PRjxeH+n3ceNfh7fE/1DuiVm4N8u5TFXmNypvRtafVzWtfxQi0VD4S09BxjbDaREk5P70c3lTsEIsl5Va4EJyX84CxU1U7j8OrXrMq6PCJhcqNhAzvUtfo5q0d1glMFjzpqGFDNK21yRCW/9gF2XnOs9GFBDccJABjauR7aSriuXekjkT2OTGpeVsFSTG40Lu7drlZtr7WTvrbejWVMdT9Z0+VF2rc55lHzG2lAx4YVWzNuwhPNNXdcdARMbjQu2LcKto/tJncYqlDJWds/h4a1quLklGjR1toh09TU5aJVEfVrYNYLbfBi+4fkDoVkoO2jOQGA3dcQkbNyq62DZmc+31q6qZ52Vt5FpnslZ8SPf8x+wTio718KkzsEh/dC+EN4snUgnJlpOiQmNxJoVcdLcdVJoyWeCnvf7yMj0EbGmUilT+yWNic//XAdCaKRzvIRlq3nY4zWW6iUQIzTqdrrLZVWZmyJhDnHnJcexpOtAqR7ASuYWx7E0s9BrmVG1IpHOQm4uTgjfpzpq2M5vqJfPCdt3Zt2wdWxc9xjCKvrw/5piXVsUKPc2i5a+PRdVZ6AVeQ3sHt8d2x8+xH4SzzgV8s/00cb17Lq32B0t4bmN5IbcxurqPsIomAuCjs4S71GyNf926C2QmqOkPK4WVnT5P2ejjsl3t/LHY011mpjjJLqLvVo4Sd3CHoznw8FAIyX8DfgCBegyjoDE1WQHNMq7U2rx6Vx9w7mL3cMxiudgrFmdGf898U2ou1fqYUbHYUaGx4Of9RD8teIKNUK27dNbRz9OAqvPird7EZrpoL/NKSdZHFIickNicLf0741M6is+y1n0S3tM75KDK/fm57+fNs66N7MD4cm9cCkJ+9OvQ2p44UnWgWiiqs4A71/fTXC6uccmiT9yc1RKSlJN3Wu97TDqtjh9Wtg+YgO2POf7vr7qropZ9nHbk1qyR2CTZTzCZJoPrGhMjEATHyiuc2vyRkJ8tsc8yjSsu+gbuklKIzwqeKKG7n5doiqfG8/3hi9QgLQ1P9uN4xX5bInk9CHvLEz6XqFX8vF2Qn/16Y2Vh28YvFz3Cs5IbiGB85fv2V115ojcnbSocjE4qz2OkJUctap7t/KkvXRyDqSfQNu3LiBAQMGwNPTE97e3hg6dChyckyv+Hv+/HnodDqjtxUrVui3M/b4smXLpHobqvSSDZWJAeCVzvVset7aNzrb9Dwp+FR1lTsE2VR2dbYosVESJ50OLWt7lTtG7et+bTC8Sz3EviNP0bmfhrTHk60DsXpUJ1leXypSjHmxprtDimTn8Ec9cOSjqApXWReTAxQDViTJkpsBAwbg2LFj2LRpE9auXYtt27ZhxIgRJrcPCgpCcnKywe3jjz9G1apV0bNnT4Ntf/rpJ4Pt+vbtK9XbUKT+7YLkDkHP090FLWt7yR2GXlN/T/yn14OBeEpq+hZL7xBlr+4ttprV3PBB7+ZoULOqLK9fz7cKZr3QBs0CPMvdjl2z8vOo5Cxavao3uzcSZT8kD0mSmxMnTmDDhg2YN28ewsPD0blzZ8yaNQvLli3D1atXjT7H2dkZ/v7+BrdVq1bh+eefR9Wqhgc1b29vg+3c3dV1UKnouh5KOmGvfaNLmfvkDm/EIw8G4pV3dVrNTovhial701qKSm5t1aqOF6Y9HVKh7swpfVqIGFHFeVZW/vfJHjOU5g1qi94hyqgxY43IZoZjS2IebyxTJMb1VOFnKidJkpv4+Hh4e3ujbdu2+vsiIyPh5OSEPXv2WLSPhIQEJCYmYujQoWUeGzVqFHx9fdG+fXssWLDAbLKQl5eHrKwsg5ua1alu34rD5XmohnJisdbyERHo1LCGqrobIhrUUFSTu6VK/0Jf6VQPL1SwLP7AiOAKPV9KakycxRLZ3A+zBzyMYx9HyR2KVb68NwVbqSKb1cKfozthw5iyF5RUliTJTUpKCmrVMsyCXVxc4OPjg5SUFIv2MX/+fDRr1gwdO3Y0uH/y5Mn49ddfsWnTJjzzzDN4/fXXMWvWrHL3NW3aNHh5eelvQUHqvvIdauPYGDLUPNATS4Z1QGiQt9yhWEys/vtKzupLkEqy14BRJxubSaVcRVotqpSa8VPPV/rxYLbWb6lZzQ1elSuhfk37jllb96bl4xV1Oh1a1fFW1EwqUxa/0l7uEKxLbsaNG2dy0O/928mTJysc1O3bt/HLL78YbbWZMGECOnXqhDZt2uD999/H2LFj8cUXX5S7v/HjxyMzM1N/u3TpUoVjtMTADnXRvam40+gefshbM2sgkXx+GNgWvnYcfF36lKO05UlMsXWpCrUVSWsRWP54ImOMzWwz5fHmfni8ufSF8mzp5pw3qC0CrSxAKsYF5kM+HmgRKM94xTcek3Y80SONa0q6f0tYlQK+8847ePnll8vdpn79+vD390daWprB/YWFhbhx4wb8/c3X4Pjtt99w69YtDBo0yOy24eHhmDJlCvLy8uDmZvyA6ebmZvIxKU25NyU7eNw60fZZ3cP2E1J50zRb1VHOoGAyTaxzZmiQN/Z/+DgW7jyHj9YcF2enFuodEoCODeSZ+vpEqwCrpoJrWcnv0uwXHwYAdJ0RZ/HzEyc+bvG2L3cMLpPwKSUBjLQh6ZrwRHPM33FOgmjso2+b2nKHIDmrLktq1qyJpk2blntzdXVFREQEMjIykJCQoH/uP//8g+LiYoSHh5t9nfnz5+Opp55CzZrms7/ExERUr15dluTF3sLr+egTJlt0qG96te6FQ+RvRhRT6zpeqOrmgjB2D5Tr5U727+J8o3tD2U5sjzWtpajSBUrh7KRDsJXdRtb8GyptOnSXRr5yh4DGfrbN/lPaZ6lUknTeNWvWDNHR0Rg+fDjmzJmDgoICjB49Gv3790dg4N1prFeuXEH37t2xePFitG//4MSalJSEbdu2Yf369WX2u2bNGqSmpqJDhw5wd3fHpk2bMHXqVLz77rtSvA3FWW5DhVVLPBdWBz5VxOuicFVAAa1Vr3dCYbGgiFiULubxxpi56V+7vZ6cA+J19+rqmBNWtzqWDrd95XUyr6KzRiviubZB2H46vcz9znZIute+0RlL917E2wqbjaU1kh35lyxZgqZNm6J79+7o1asXOnfujB9//FH/eEFBAU6dOoVbt24ZPG/BggWoU6cOevQoW/a8UqVKmD17NiIiIhAaGooffvgBM2fOxKRJk6R6G5IoPdBOTo39quLDClQmNqaSsxNe6lCxmTAV5eSkY2JjoTe7N8KM51rb7fXUMCCyT2ig1d8ftYwj0qL3oppgwcttzW9oxlf9QuFb1RXTng4RISrjWtb2wqf/FwLfqvy+SEmyo4yPjw9++eUXk48HBwcbzdynTp2KqVOnGn1OdHQ0oqOjRYvRXl7uGIyFu87r//5hYBjGLEvE2OimGL54v93iGNWtYZky9h/0bm7VwEBLyTVQjmzj52l4oA2rWx0JF27KFI15/l7S1bZ6of1D6N/OeHLeqJbp1bqn/l8IHv0iDoD8tZ4s4VJi8K091lCS0qhuDa3a3tQxr2VtL+z7IFIx44HIdsq/hNKAj55qgTYPeeOtZYkA7p74N8XYv5R8xwa+SJz4ONwrOaPphA12f32iivrttQh8E3sak54Ur7WxiV81nErNBgD0axtU7lV7zWpu+OedR422PtWtUQUrXouAq7OTKmoRuTg7YfmIDsgvKoaXh/2TG3smEH1CA/FH4oMCso808sXLHYPR3EjVaSY22sB2ezvxcFVGHunt4SrLVPL2waYHM9vLh72bAbjbhE2GSleuFXvarq1TqktrG+yD/w0NR8NyWlBssTnmUYyJbIQPn2hmdtv6NauilomlFtoF+6C1iuomhdevgS6N5Jm2a2sKERrkjb/HPGLVc6b+n2HCqtPp8NFTLfC8Bqp9k3FMbshi/drafiAY8Uh9ESOxzbAu9bH/w0irm7AdQcmL1TkvPYxhIheKXDikHfw83fD9gIdF3a9YGtaqijGRjVFN5d0zSiWUqVFtu8+fbYUm/tXw2TOWj4up4uaCajKN9eoT6lhrwSkFkxuVeL5tHblDwJS+LW2eQtm9mbjFDG3FQXzmRbcMKHeVblu0DfbBnv9EKnJ9HDFPvEqnhR6Xxn53W+1MtZ6Z8lW/UAB3a9TYS1QLP0S1MF/bzRreMnQhqhGTGztpX+9ut4yt5b0n97G9vo1YXF2c8L+h4XjMyqrLY6ObsB/bCk+1Nn2l95CPetfyIpJTZHM/nPok2q7L17QI9BL92FfNvRL+UNF6eHJhcmMnXpUr4fjkKGy0sq8YAHaNe0xRSy682f1u6W5LV6d+vSu7gazx7QttTD7mZ+XVakWM6tbAotWdN8swOL6i4t7tKncImhJR/27F6WfDLG9hrmhrrr8NvwU3F+UcRytCTeO65MLkxo48XF2sau7/9oU2ODDhcavXPZFaaJA3jn0cJWktCLIvY9eW70U1xWwLxsg0rGVbpVU5WVuNl8r3w6AwzH7xYXz8lOUtzM2MzFQCLB/b1yzAE9OeDsHCIe0sfk17k7JO4Tf9Q6XbeSn/6dUU0S38cWJy2VIswzrXw8dPtbBbLJZicqNgT7UOFLVysDHBNWzr5qji5sKuJgfU2K+qZpLa+91/bFmsOE/3SujdKgCVXY23jATXsDyZHPGo6ckHnz/byuDvF9o/hK5NlDGezxgpx3P1Ca2N2Hfs02o64pEGmDMwDJVdnRHZzHAmZb92QRjcMVhxs1CVMT/ZgdX2rowrGbcBlL+wpSU+7N3MotLyAPD3mEdwPTcPda046JBjeqJVANYeTgYA/PXWI3B20iGkthfGLE/EWIUd0Kzxdb9QjI1uIutyEFq3OeYR3MgtQJCFY8VmPNcagV4PWqqn9GmBCX8cAwAsG9EBHerLs+CqUjWoaf9W03mD20IQBHyw+ihu5OTrW249TCS2cmFyI7Mqbg++EGem9sLQhfsQezKtnGeYNqyL5dOtm/hXAyBurZCSXCwsYvbO443xpR3XNVI7qa4Dy5t5MvXpENT2rownWwfC+d6/a8vaXqoca1OSk5OOiU0pPw8Nx0vz94i2P2vrEfUNDURhiQu8x5r5oZp7JTTxr2ayG0tpvurXGm8vPwTAPotcvvpoffyw9az+76Gd60m+YrlOpytTO0hp2C2lMN+80AbvPN5Y9SeOJ1sHonUdL7xqpr7NyK4N8Eqnevj8mVaoIXEXnFrNHVTxNXPMaVirKmY81xqLXim7OryneyWM72V5qyCpV+dGvvCtKu/v0L2SM157tAEGR9RFbe/K6NumtqITm/o1q2D72G5wdXbCC+2D8H9tHgyqtkeRgfE9m+HctF5mtxsQfndJkU/6SjPzVmkXCmy5kVljv2r4NzVH/3dVNxe8cW82kiWGd6mHudulzdJt4V7JGX+M7mx2OxdnJ0y8V0r/2bA6WHP4qn6ZinbB1TG+VzO8snAf+obWljJcxarm7iJ6tWBTrJnpQlom/1i6cT2byh2CxcLr+SDIxwPHJkeJVonbWqXHP/Zs6Y+TKdnYMKYLtv+bjiAfDzTxr4axUU0lW2ojslktjI1ughCFXAQxuZHZlD4t4VPFFc+Fab8MuLnFGJ2cdHiyVSBOp+YgLLg6ut0bKHjgw8dVsVaPmFa8FoFp608oor4ROZZa1dyQnpNn19dsFuCJqm7O+m5PNWlb924Ns5KJTXWPSrh5qwDdrawJJpbvBjwMQbh7TI0scXEk5RpiOp1OUYPzmdzIrHoVV4c5gbWu4212pWknJx3eLTVI1dESG+DuGkUrXy9bqMvSsUxEtpo94GG8NG+PfqIDADSoWQVnruVK9prr3ugMnU5di1ZuH9sNBy9l4AkjtaC2v/8YUjJvi74GmqV0Op0mqlFXBMfckCw+f6aV1YvfObLXHm2APqGBaBGo3LEHpHyDI4IB3J0BZ0o93yrYOe4xhNWtrr9v6fAO6BsaiCl9WyLAyx3zB4s7DszJSaeqxAYAgnw88FTrQKMXX1XdXGRLbOguttyonFpbNbgar3XUNAaBlOuhGh44OSUabi7mr2sHdqiLhAs30b6eD2p5uuPr/m3094uhYwPb1qmj8rlXYpsFwORGtV5oH4SUzDtoruBZBKW9+mh9rDx4Gc88zIGrRHKxdCmXPqGBaB7oibo2Fvo0J6SOF9a+0RkBXvZbUkTLJj7RHH8cuooRjzSQOxRF0AmCPWbiK0tWVha8vLyQmZkJT0/1JAfGnEvPRbcZcQCA89N7yxuMBYqLBdW2NhERkbwsPX+z5Ubl6vlWwdo3OsO3qpvcoViEiQ0REUmNyY0GsLgaERHRAxx5RERERJrC5IaIiIg0hckNERERaQqTGyIiItIUJjdERESkKUxuiIiISFOY3BAREZGmMLkhIiIiTWFyQ0RERJrC5IaIiIg0hckNERERaQqTGyIiItIUJjdERESkKQ65KrggCACArKwsmSMhIiIiS90/b98/j5vikMlNdnY2ACAoKEjmSIiIiMha2dnZ8PLyMvm4TjCX/mhQcXExrl69imrVqkGn04m676ysLAQFBeHSpUvw9PQUdd/0AD9n++DnbB/8nO2Dn7N9SPk5C4KA7OxsBAYGwsnJ9Mgah2y5cXJyQp06dSR9DU9PT/547ICfs33wc7YPfs72wc/ZPqT6nMtrsbmPA4qJiIhIU5jcEBERkaYwuRGZm5sbJk2aBDc3N7lD0TR+zvbBz9k++DnbBz9n+1DC5+yQA4qJiIhIu9hyQ0RERJrC5IaIiIg0hckNERERaQqTGyIiItIUJjdWmj17NoKDg+Hu7o7w8HDs3bu33O1XrFiBpk2bwt3dHSEhIVi/fr2dIlU/az7ruXPnokuXLqhevTqqV6+OyMhIs/82dJe13+n7li1bBp1Oh759+0oboEZY+zlnZGRg1KhRCAgIgJubGxo3bszjhwWs/Zy//vprNGnSBJUrV0ZQUBDefvtt3Llzx07RqtO2bdvw5JNPIjAwEDqdDqtXrzb7nLi4ODz88MNwc3NDw4YNsXDhQmmDFMhiy5YtE1xdXYUFCxYIx44dE4YPHy54e3sLqampRrffuXOn4OzsLHz++efC8ePHhQ8//FCoVKmScOTIETtHrj7WftYvvviiMHv2bOHgwYPCiRMnhJdfflnw8vISLl++bOfI1cXaz/m+c+fOCbVr1xa6dOki9OnTxz7Bqpi1n3NeXp7Qtm1boVevXsKOHTuEc+fOCXFxcUJiYqKdI1cXaz/nJUuWCG5ubsKSJUuEc+fOCX///bcQEBAgvP3223aOXF3Wr18vfPDBB8LKlSsFAMKqVavK3f7s2bOCh4eHEBMTIxw/flyYNWuW4OzsLGzYsEGyGJncWKF9+/bCqFGj9H8XFRUJgYGBwrRp04xu//zzzwu9e/c2uC88PFx49dVXJY1TC6z9rEsrLCwUqlWrJixatEiqEDXBls+5sLBQ6NixozBv3jxh8ODBTG4sYO3n/P333wv169cX8vPz7RWiJlj7OY8aNUp47LHHDO6LiYkROnXqJGmcWmJJcjN27FihRYsWBvf169dPiIqKkiwudktZKD8/HwkJCYiMjNTf5+TkhMjISMTHxxt9Tnx8vMH2ABAVFWVye7rLls+6tFu3bqGgoAA+Pj5Shal6tn7OkydPRq1atTB06FB7hKl6tnzOf/75JyIiIjBq1Cj4+fmhZcuWmDp1KoqKiuwVturY8jl37NgRCQkJ+q6rs2fPYv369ejVq5ddYnYUcpwLHXLhTFukp6ejqKgIfn5+Bvf7+fnh5MmTRp+TkpJidPuUlBTJ4tQCWz7r0t5//30EBgaW+UHRA7Z8zjt27MD8+fORmJhohwi1wZbP+ezZs/jnn38wYMAArF+/HklJSXj99ddRUFCASZMm2SNs1bHlc37xxReRnp6Ozp07QxAEFBYW4rXXXsN//vMfe4TsMEydC7OysnD79m1UrlxZ9Ndkyw1pzvTp07Fs2TKsWrUK7u7ucoejGdnZ2Rg4cCDmzp0LX19fucPRtOLiYtSqVQs//vgjwsLC0K9fP3zwwQeYM2eO3KFpSlxcHKZOnYrvvvsOBw4cwMqVK7Fu3TpMmTJF7tCogthyYyFfX184OzsjNTXV4P7U1FT4+/sbfY6/v79V29NdtnzW982YMQPTp0/H5s2b0apVKynDVD1rP+czZ87g/PnzePLJJ/X3FRcXAwBcXFxw6tQpNGjQQNqgVciW73NAQAAqVaoEZ2dn/X3NmjVDSkoK8vPz4erqKmnMamTL5zxhwgQMHDgQw4YNAwCEhIQgNzcXI0aMwAcffAAnJ17/i8HUudDT01OSVhuALTcWc3V1RVhYGGJjY/X3FRcXIzY2FhEREUafExERYbA9AGzatMnk9nSXLZ81AHz++eeYMmUKNmzYgLZt29ojVFWz9nNu2rQpjhw5gsTERP3tqaeeQrdu3ZCYmIigoCB7hq8atnyfO3XqhKSkJH3yCAD//vsvAgICmNiYYMvnfOvWrTIJzP2EUuCyi6KR5Vwo2VBlDVq2bJng5uYmLFy4UDh+/LgwYsQIwdvbW0hJSREEQRAGDhwojBs3Tr/9zp07BRcXF2HGjBnCiRMnhEmTJnEquIWs/aynT58uuLq6Cr/99puQnJysv2VnZ8v1FlTB2s+5NM6Wsoy1n/PFixeFatWqCaNHjxZOnTolrF27VqhVq5bwySefyPUWVMHaz3nSpElCtWrVhKVLlwpnz54VNm7cKDRo0EB4/vnn5XoLqpCdnS0cPHhQOHjwoABAmDlzpnDw4EHhwoULgiAIwrhx44SBAwfqt78/Ffy9994TTpw4IcyePZtTwZVm1qxZwkMPPSS4uroK7du3F3bv3q1/7NFHHxUGDx5ssP2vv/4qNG7cWHB1dRVatGghrFu3zs4Rq5c1n3XdunUFAGVukyZNsn/gKmPtd7okJjeWs/Zz3rVrlxAeHi64ubkJ9evXFz799FOhsLDQzlGrjzWfc0FBgfDRRx8JDRo0ENzd3YWgoCDh9ddfF27evGn/wFVky5YtRo+39z/bwYMHC48++miZ54SGhgqurq5C/fr1hZ9++knSGHWCwLY3IiIi0g6OuSEiIiJNYXJDREREmsLkhoiIiDSFyQ0RERFpCpMbIiIi0hQmN0RERKQpTG6IiIhIU5jcEBERkSi2bduGJ598EoGBgdDpdFi9erXV+xAEATNmzEDjxo3h5uaG2rVr49NPP7VqH1w4k4iIiESRm5uL1q1b45VXXsHTTz9t0z7eeustbNy4ETNmzEBISAhu3LiBGzduWLUPVigmIiIi0el0OqxatQp9+/bV35eXl4cPPvgAS5cuRUZGBlq2bInPPvsMXbt2BQCcOHECrVq1wtGjR9GkSRObX5vdUkRERGQXo0ePRnx8PJYtW4bDhw/jueeeQ3R0NE6fPg0AWLNmDerXr4+1a9eiXr16CA4OxrBhw6xuuWFyQ0RERJK7ePEifvrpJ6xYsQJdunRBgwYN8O6776Jz58746aefAABnz57FhQsXsGLFCixevBgLFy5EQkICnn32Watei2NuiIiISHJHjhxBUVERGjdubHB/Xl4eatSoAQAoLi5GXl4eFi9erN9u/vz5CAsLw6lTpyzuqmJyQ0RERJLLycmBs7MzEhIS4OzsbPBY1apVAQABAQFwcXExSICaNWsG4G7LD5MbIiIiUow2bdqgqKgIaWlp6NKli9FtOnXqhMLCQpw5cwYNGjQAAPz7778AgLp161r8WpwtRURERKLIyclBUlISgLvJzMyZM9GtWzf4+PjgoYcewksvvYSdO3fiyy+/RJs2bXDt2jXExsaiVatW6N27N4qLi9GuXTtUrVoVX3/9NYqLizFq1Ch4enpi48aNFsfB5IaIiIhEERcXh27dupW5f/DgwVi4cCEKCgrwySefYPHixbhy5Qp8fX3RoUMHfPzxxwgJCQEAXL16FW+88QY2btyIKlWqoGfPnvjyyy/h4+NjcRxMboiIiEhTOBWciIiINIXJDREREWkKkxsiIiLSFCY3REREpClMboiIiEhTmNwQERGRpjC5ISIiIk1hckNERESawuSGiIiINIXJDREREWkKkxsiIiLSFCY3REREpCn/D9dxvBKlgJGwAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.signal import resample_poly, firwin, bilinear, lfilter\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Read in signal\n",
    "x = np.fromfile(\"fm_rds_250k_1Msamples.iq\", np.complex64)\n",
    "sample_rate = 250e3\n",
    "fc = 99.5e6\n",
    "\n",
    "# Quadrature Demod\n",
    "x = 0.5 * np.angle(x[0:-1] * np.conj(x[1:])) # see https://wiki.gnuradio.org/index.php/Quadrature_Demod\n",
    "plt.plot(x)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As discussed at the beginning of this chapter, several individual signals are combined in frequency and FM modulated to create what is actually transmitted through the air. So the first step is to undo that FM modulation. Another way to think about it is the information is stored in the frequency variation of the signal we receive, and we want to demodulate it so the information is now in the amplitude not frequency. Note that the output of this demodulation is a real signal, even though we fed in a complex signal.\n",
    "\n",
    "What this single line of Python is doing, is first calculating the product of our signal with a delayed and conjugated version of our signal. Next, it finds the phase of each sample in that result, which is the moment at which it goes from complex to real. To prove to ourselves that this gives us the information contained in the frequency variations, consider a tone at frequency f with some arbitrary phase \\phi, which we can represent as e^{j2 \\pi (f t + \\phi)}. When dealing in discrete time, which uses an integer n instead of t, this becomes e^{j2 \\pi (f n + \\phi)}. The conjugated and delayed version is e^{-j2 \\pi (f (n-1) + \\phi)}. Multiplying these two together leads to e^{j2 \\pi f}, which is great because \\phi is gone, and when we calculate the phase of that expression we are left with just f.\n",
    "\n",
    "One convenient side effect of FM modulation is that amplitude variations of the received signal does not actually change the volume of the audio, unlike AM radio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/codespace/.local/lib/python3.10/site-packages/matplotlib/cbook/__init__.py:1335: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  return np.asarray(x, float)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8a80639030>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjgAAAGvCAYAAABW/q+QAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAtdklEQVR4nO3dfVSVdb7//xegbKQEdBA2FIU35c14g4NJmJ1u3EtIVpPrNKWThXIMTqY1iqUwpVZWmJnLY8MMJ/N2jWVjJzuVfkmimFZF2mCcrMjJ1LR0o+bAFixur98f/drNHkHdxoXuT8/HWtfK/bne1+f6fD5Z+7WufV17B1mWZQkAAMAgwed6AAAAAB2NgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAME6Xcz2Ac6G1tVUHDx5U9+7dFRQUdK6HAwAAzoBlWTp+/Lji4+MVHHzqazQ/y4Bz8OBBJSQknOthAACAs3DgwAFdfPHFp6z5WQac7t27S/p+gSIiIs7xaAAAwJnweDxKSEjwvo+fys8y4PzwsVRERAQBBwCAAHMmt5dwkzEAADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMI6tAeftt9/WjTfeqPj4eAUFBenll18+7TFlZWX61a9+JYfDoX79+mnNmjUn1RQWFioxMVFhYWFKSUnR9u3bO37wAAAgYNkacOrr6zVs2DAVFhaeUf3evXuVkZGh6667TpWVlZo5c6buvPNOvf76696aF154Qbm5uVqwYIF27NihYcOGKS0tTYcPH7ZrGgAAIMAEWZZldcqJgoK0adMmjR8/vt2auXPnavPmzfr444+9bRMnTlRNTY2Ki4slSSkpKbriiiv0hz/8QZLU2tqqhIQE3XPPPcrLyzujsXg8HkVGRqq2tpbfogIAIED48/59Xv3YZnl5uVwul09bWlqaZs6cKUlqbGxURUWF8vPzvfuDg4PlcrlUXl7ebr8NDQ1qaGjwvvZ4PB078P/f/9t5SNPW77ClbwAAAknR7clKH+w8Z+c/r24ydrvdio2N9WmLjY2Vx+PRt99+q6NHj6qlpaXNGrfb3W6/BQUFioyM9G4JCQm2jJ9wAwDA9+76c8U5Pf95FXDskp+fr9raWu924MCBcz0kAABgo/PqIyqn06nq6mqfturqakVERKhbt24KCQlRSEhImzVOZ/uXwRwOhxwOhy1jBgAA55/z6gpOamqqSktLfdpKSkqUmpoqSQoNDVVycrJPTWtrq0pLS701AAAAtgacuro6VVZWqrKyUtL3j4FXVlZq//79kr7/6CgzM9Nbf9ddd2nPnj2aM2eOPvvsM/3xj3/UX/7yF82aNctbk5ubqxUrVmjt2rWqqqrStGnTVF9fr6ysLDunAgAAAoitH1H97W9/03XXXed9nZubK0maPHmy1qxZo0OHDnnDjiT17t1bmzdv1qxZs/Rf//Vfuvjii/Xss88qLS3NWzNhwgQdOXJE8+fPl9vtVlJSkoqLi0+68RgAAPx8ddr34JxP7PoenMS8zR3WFwAAgW7foowO7c+f9+/z6h4cAACAjkDAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwTqcEnMLCQiUmJiosLEwpKSnavn17u7XXXnutgoKCTtoyMjK8NVOmTDlpf3p6emdMBQAABIAudp/ghRdeUG5uroqKipSSkqJly5YpLS1Nu3btUkxMzEn1L730khobG72vv/nmGw0bNky33HKLT116erpWr17tfe1wOOybBAAACCi2X8FZunSpsrOzlZWVpUGDBqmoqEjh4eFatWpVm/U9e/aU0+n0biUlJQoPDz8p4DgcDp+6Hj162D0VAAAQIGwNOI2NjaqoqJDL5frxhMHBcrlcKi8vP6M+Vq5cqYkTJ+qCCy7waS8rK1NMTIz69++vadOm6Ztvvmm3j4aGBnk8Hp8NAACYy9aAc/ToUbW0tCg2NtanPTY2Vm63+7THb9++XR9//LHuvPNOn/b09HStW7dOpaWleuKJJ/TXv/5VN9xwg1paWtrsp6CgQJGRkd4tISHh7CcFAADOe7bfg/NTrFy5UkOGDNHIkSN92idOnOj985AhQzR06FD17dtXZWVlGjNmzEn95OfnKzc31/va4/EQcgAAMJitV3Cio6MVEhKi6upqn/bq6mo5nc5THltfX68NGzZo6tSppz1Pnz59FB0drd27d7e53+FwKCIiwmcDAADmsjXghIaGKjk5WaWlpd621tZWlZaWKjU19ZTHbty4UQ0NDbr99ttPe56vvvpK33zzjeLi4n7ymAEAQOCz/Smq3NxcrVixQmvXrlVVVZWmTZum+vp6ZWVlSZIyMzOVn59/0nErV67U+PHj9Ytf/MKnva6uTvfff7/ef/997du3T6WlpbrpppvUr18/paWl2T0dAAAQAGy/B2fChAk6cuSI5s+fL7fbraSkJBUXF3tvPN6/f7+Cg31z1q5du/TOO+9o69atJ/UXEhKijz76SGvXrlVNTY3i4+M1duxYLVy4kO/CAQAAkqQgy7Kscz2IzubxeBQZGana2toOvR8nMW9zh/UFAECg27co4/RFfvDn/ZvfogIAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGKdTAk5hYaESExMVFhamlJQUbd++vd3aNWvWKCgoyGcLCwvzqbEsS/Pnz1dcXJy6desml8ulzz//3O5pAACAAGF7wHnhhReUm5urBQsWaMeOHRo2bJjS0tJ0+PDhdo+JiIjQoUOHvNuXX37ps3/x4sVavny5ioqKtG3bNl1wwQVKS0vTd999Z/d0AABAALA94CxdulTZ2dnKysrSoEGDVFRUpPDwcK1atardY4KCguR0Or1bbGysd59lWVq2bJkefPBB3XTTTRo6dKjWrVungwcP6uWXX7Z7OgAAIADYGnAaGxtVUVEhl8v14wmDg+VyuVReXt7ucXV1dbr00kuVkJCgm266SZ988ol33969e+V2u336jIyMVEpKSrt9NjQ0yOPx+GwAAMBctgaco0ePqqWlxecKjCTFxsbK7Xa3eUz//v21atUq/e///q/+/Oc/q7W1VaNGjdJXX30lSd7j/OmzoKBAkZGR3i0hIeGnTg0AAJzHzrunqFJTU5WZmamkpCRdc801eumll9SrVy/993//91n3mZ+fr9raWu924MCBDhwxAAA439gacKKjoxUSEqLq6mqf9urqajmdzjPqo2vXrho+fLh2794tSd7j/OnT4XAoIiLCZwMAAOayNeCEhoYqOTlZpaWl3rbW1laVlpYqNTX1jPpoaWnRzp07FRcXJ0nq3bu3nE6nT58ej0fbtm074z4BAIDZuth9gtzcXE2ePFkjRozQyJEjtWzZMtXX1ysrK0uSlJmZqYsuukgFBQWSpEceeURXXnml+vXrp5qaGj355JP68ssvdeedd0r6/gmrmTNn6tFHH9Vll12m3r17a968eYqPj9f48ePtng4AAAgAtgecCRMm6MiRI5o/f77cbreSkpJUXFzsvUl4//79Cg7+8ULSP/7xD2VnZ8vtdqtHjx5KTk7We++9p0GDBnlr5syZo/r6euXk5KimpkajR49WcXHxSV8ICAAAfp6CLMuyzvUgOpvH41FkZKRqa2s79H6cxLzNHdYXAACBbt+ijA7tz5/37/PuKSoAAICfioADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGCcTgk4hYWFSkxMVFhYmFJSUrR9+/Z2a1esWKGrr75aPXr0UI8ePeRyuU6qnzJlioKCgny29PR0u6cBAAAChO0B54UXXlBubq4WLFigHTt2aNiwYUpLS9Phw4fbrC8rK9Nvf/tbvfXWWyovL1dCQoLGjh2rr7/+2qcuPT1dhw4d8m7PP/+83VMBAAABwvaAs3TpUmVnZysrK0uDBg1SUVGRwsPDtWrVqjbr169fr7vvvltJSUkaMGCAnn32WbW2tqq0tNSnzuFwyOl0ercePXrYPRUAABAgbA04jY2NqqiokMvl+vGEwcFyuVwqLy8/oz5OnDihpqYm9ezZ06e9rKxMMTEx6t+/v6ZNm6Zvvvmm3T4aGhrk8Xh8NgAAYC5bA87Ro0fV0tKi2NhYn/bY2Fi53e4z6mPu3LmKj4/3CUnp6elat26dSktL9cQTT+ivf/2rbrjhBrW0tLTZR0FBgSIjI71bQkLC2U8KAACc97qc6wGcyqJFi7RhwwaVlZUpLCzM2z5x4kTvn4cMGaKhQ4eqb9++Kisr05gxY07qJz8/X7m5ud7XHo+HkAMAgMFsvYITHR2tkJAQVVdX+7RXV1fL6XSe8tglS5Zo0aJF2rp1q4YOHXrK2j59+ig6Olq7d+9uc7/D4VBERITPBgAAzGVrwAkNDVVycrLPDcI/3DCcmpra7nGLFy/WwoULVVxcrBEjRpz2PF999ZW++eYbxcXFdci4AQBAYLP9Karc3FytWLFCa9euVVVVlaZNm6b6+nplZWVJkjIzM5Wfn++tf+KJJzRv3jytWrVKiYmJcrvdcrvdqqurkyTV1dXp/vvv1/vvv699+/aptLRUN910k/r166e0tDS7pwMAAAKA7ffgTJgwQUeOHNH8+fPldruVlJSk4uJi743H+/fvV3DwjznrT3/6kxobG/Wb3/zGp58FCxbooYceUkhIiD766COtXbtWNTU1io+P19ixY7Vw4UI5HA67pwMAAAJAkGVZ1rkeRGfzeDyKjIxUbW1th96Pk5i3ucP6AgAg0O1blNGh/fnz/s1vUQEAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjNMpAaewsFCJiYkKCwtTSkqKtm/ffsr6jRs3asCAAQoLC9OQIUO0ZcsWn/2WZWn+/PmKi4tTt27d5HK59Pnnn9s5BQAAEEBsDzgvvPCCcnNztWDBAu3YsUPDhg1TWlqaDh8+3Gb9e++9p9/+9reaOnWqPvzwQ40fP17jx4/Xxx9/7K1ZvHixli9frqKiIm3btk0XXHCB0tLS9N1339k9HQAAEACCLMuy7DxBSkqKrrjiCv3hD3+QJLW2tiohIUH33HOP8vLyTqqfMGGC6uvr9dprr3nbrrzySiUlJamoqEiWZSk+Pl6zZ8/WfffdJ0mqra1VbGys1qxZo4kTJ552TB6PR5GRkaqtrVVEREQHzVRKzNvcYX0BABDo9i3K6ND+/Hn/tvUKTmNjoyoqKuRyuX48YXCwXC6XysvL2zymvLzcp16S0tLSvPV79+6V2+32qYmMjFRKSkq7fTY0NMjj8fhsAADAXLYGnKNHj6qlpUWxsbE+7bGxsXK73W0e43a7T1n/wz/96bOgoECRkZHeLSEh4azmAwAAAsPP4imq/Px81dbWercDBw6c6yEBAAAb2RpwoqOjFRISourqap/26upqOZ3ONo9xOp2nrP/hn/706XA4FBER4bMBAABz2RpwQkNDlZycrNLSUm9ba2urSktLlZqa2uYxqampPvWSVFJS4q3v3bu3nE6nT43H49G2bdva7RMAAPy8dLH7BLm5uZo8ebJGjBihkSNHatmyZaqvr1dWVpYkKTMzUxdddJEKCgokSb/73e90zTXX6KmnnlJGRoY2bNigv/3tb3rmmWckSUFBQZo5c6YeffRRXXbZZerdu7fmzZun+Ph4jR8/3u7pAACAAGB7wJkwYYKOHDmi+fPny+12KykpScXFxd6bhPfv36/g4B8vJI0aNUrPPfecHnzwQf3+97/XZZddppdfflmDBw/21syZM0f19fXKyclRTU2NRo8ereLiYoWFhdk9HQAAEABs/x6c8xHfgwMAgP2M/R4cAACAc4GAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHFsDzrFjxzRp0iRFREQoKipKU6dOVV1d3Snr77nnHvXv31/dunXTJZdconvvvVe1tbU+dUFBQSdtGzZssHMqAAAggHSxs/NJkybp0KFDKikpUVNTk7KyspSTk6PnnnuuzfqDBw/q4MGDWrJkiQYNGqQvv/xSd911lw4ePKgXX3zRp3b16tVKT0/3vo6KirJzKgAAIIDYFnCqqqpUXFysDz74QCNGjJAkPf300xo3bpyWLFmi+Pj4k44ZPHiw/ud//sf7um/fvnrsscd0++23q7m5WV26/DjcqKgoOZ1Ou4YPAAACmG0fUZWXlysqKsobbiTJ5XIpODhY27ZtO+N+amtrFRER4RNuJGn69OmKjo7WyJEjtWrVKlmW1W4fDQ0N8ng8PhsAADCXbVdw3G63YmJifE/WpYt69uwpt9t9Rn0cPXpUCxcuVE5Ojk/7I488ouuvv17h4eHaunWr7r77btXV1enee+9ts5+CggI9/PDDZzcRAAAQcPy+gpOXl9fmTb7/vH322Wc/eWAej0cZGRkaNGiQHnroIZ998+bN01VXXaXhw4dr7ty5mjNnjp588sl2+8rPz1dtba13O3DgwE8eHwAAOH/5fQVn9uzZmjJlyilr+vTpI6fTqcOHD/u0Nzc369ixY6e9d+b48eNKT09X9+7dtWnTJnXt2vWU9SkpKVq4cKEaGhrkcDhO2u9wONpsBwAAZvI74PTq1Uu9evU6bV1qaqpqampUUVGh5ORkSdKbb76p1tZWpaSktHucx+NRWlqaHA6HXnnlFYWFhZ32XJWVlerRowchBgAASLLxHpyBAwcqPT1d2dnZKioqUlNTk2bMmKGJEyd6n6D6+uuvNWbMGK1bt04jR46Ux+PR2LFjdeLECf35z3/2uSG4V69eCgkJ0auvvqrq6mpdeeWVCgsLU0lJiR5//HHdd999dk0FAAAEGFu/B2f9+vWaMWOGxowZo+DgYN18881avny5d39TU5N27dqlEydOSJJ27NjhfcKqX79+Pn3t3btXiYmJ6tq1qwoLCzVr1ixZlqV+/fpp6dKlys7OtnMqAAAggARZp3q+2lAej0eRkZHeR9A7SmLe5g7rCwCAQLdvUUaH9ufP+ze/RQUAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMI6tAefYsWOaNGmSIiIiFBUVpalTp6quru6Ux1x77bUKCgry2e666y6fmv379ysjI0Ph4eGKiYnR/fffr+bmZjunAgAAAkgXOzufNGmSDh06pJKSEjU1NSkrK0s5OTl67rnnTnlcdna2HnnkEe/r8PBw759bWlqUkZEhp9Op9957T4cOHVJmZqa6du2qxx9/3La5AACAwGFbwKmqqlJxcbE++OADjRgxQpL09NNPa9y4cVqyZIni4+PbPTY8PFxOp7PNfVu3btWnn36qN954Q7GxsUpKStLChQs1d+5cPfTQQwoNDbVlPgAAIHDY9hFVeXm5oqKivOFGklwul4KDg7Vt27ZTHrt+/XpFR0dr8ODBys/P14kTJ3z6HTJkiGJjY71taWlp8ng8+uSTT9rsr6GhQR6Px2cDAADmsu0KjtvtVkxMjO/JunRRz5495Xa72z3utttu06WXXqr4+Hh99NFHmjt3rnbt2qWXXnrJ2+8/hxtJ3tft9VtQUKCHH374p0wHAAAEEL8DTl5enp544olT1lRVVZ31gHJycrx/HjJkiOLi4jRmzBh98cUX6tu371n1mZ+fr9zcXO9rj8ejhISEsx4jAAA4v/kdcGbPnq0pU6acsqZPnz5yOp06fPiwT3tzc7OOHTvW7v01bUlJSZEk7d69W3379pXT6dT27dt9aqqrqyWp3X4dDoccDscZnxMAAAQ2vwNOr1691KtXr9PWpaamqqamRhUVFUpOTpYkvfnmm2ptbfWGljNRWVkpSYqLi/P2+9hjj+nw4cPej8BKSkoUERGhQYMG+TkbAABgIttuMh44cKDS09OVnZ2t7du3691339WMGTM0ceJE7xNUX3/9tQYMGOC9IvPFF19o4cKFqqio0L59+/TKK68oMzNT//Zv/6ahQ4dKksaOHatBgwbpjjvu0P/93//p9ddf14MPPqjp06dzlQYAAEiy+Yv+1q9frwEDBmjMmDEaN26cRo8erWeeeca7v6mpSbt27fI+JRUaGqo33nhDY8eO1YABAzR79mzdfPPNevXVV73HhISE6LXXXlNISIhSU1N1++23KzMz0+d7cwAAwM9bkGVZ1rkeRGfzeDyKjIxUbW2tIiIiOqzfxLzNHdYXAACBbt+ijA7tz5/3b36LCgAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHFsDzrFjxzRp0iRFREQoKipKU6dOVV1dXbv1+/btU1BQUJvbxo0bvXVt7d+wYYOdUwEAAAGki52dT5o0SYcOHVJJSYmampqUlZWlnJwcPffcc23WJyQk6NChQz5tzzzzjJ588kndcMMNPu2rV69Wenq693VUVFSHjx8AAAQm2wJOVVWViouL9cEHH2jEiBGSpKefflrjxo3TkiVLFB8ff9IxISEhcjqdPm2bNm3SrbfeqgsvvNCnPSoq6qRaAAAAycaPqMrLyxUVFeUNN5LkcrkUHBysbdu2nVEfFRUVqqys1NSpU0/aN336dEVHR2vkyJFatWqVLMtqt5+GhgZ5PB6fDQAAmMu2Kzhut1sxMTG+J+vSRT179pTb7T6jPlauXKmBAwdq1KhRPu2PPPKIrr/+eoWHh2vr1q26++67VVdXp3vvvbfNfgoKCvTwww+f3UQAAEDA8fsKTl5eXrs3Av+wffbZZz95YN9++62ee+65Nq/ezJs3T1dddZWGDx+uuXPnas6cOXryySfb7Ss/P1+1tbXe7cCBAz95fAAA4Pzl9xWc2bNna8qUKaes6dOnj5xOpw4fPuzT3tzcrGPHjp3RvTMvvviiTpw4oczMzNPWpqSkaOHChWpoaJDD4Thpv8PhaLMdAACYye+A06tXL/Xq1eu0dampqaqpqVFFRYWSk5MlSW+++aZaW1uVkpJy2uNXrlypX//612d0rsrKSvXo0YMQAwAAJNl4D87AgQOVnp6u7OxsFRUVqampSTNmzNDEiRO9T1B9/fXXGjNmjNatW6eRI0d6j929e7fefvttbdmy5aR+X331VVVXV+vKK69UWFiYSkpK9Pjjj+u+++6zayoAACDA2Po9OOvXr9eMGTM0ZswYBQcH6+abb9by5cu9+5uamrRr1y6dOHHC57hVq1bp4osv1tixY0/qs2vXriosLNSsWbNkWZb69eunpUuXKjs7286pAACAABJkner5akN5PB5FRkaqtrZWERERHdZvYt7mDusLAIBAt29RRof258/7N79FBQAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwjm0B57HHHtOoUaMUHh6uqKioMzrGsizNnz9fcXFx6tatm1wulz7//HOfmmPHjmnSpEmKiIhQVFSUpk6dqrq6OhtmAAAAApVtAaexsVG33HKLpk2bdsbHLF68WMuXL1dRUZG2bdumCy64QGlpafruu++8NZMmTdInn3yikpISvfbaa3r77beVk5NjxxQAAECACrIsy7LzBGvWrNHMmTNVU1NzyjrLshQfH6/Zs2frvvvukyTV1tYqNjZWa9as0cSJE1VVVaVBgwbpgw8+0IgRIyRJxcXFGjdunL766ivFx8ef0Zg8Ho8iIyNVW1uriIiInzS/f5aYt7nD+gIAINDtW5TRof358/593tyDs3fvXrndbrlcLm9bZGSkUlJSVF5eLkkqLy9XVFSUN9xIksvlUnBwsLZt29Zu3w0NDfJ4PD4bAAAw13kTcNxutyQpNjbWpz02Nta7z+12KyYmxmd/ly5d1LNnT29NWwoKChQZGendEhISOnj037tx2JldQQIAAPbyK+Dk5eUpKCjolNtnn31m11jPWn5+vmpra73bgQMHbDnP8olJtvQLAECg+Wxh+jk9fxd/imfPnq0pU6acsqZPnz5nNRCn0ylJqq6uVlxcnLe9urpaSUlJ3prDhw/7HNfc3Kxjx455j2+Lw+GQw+E4q3H5IygoqMM/bwQAAP7zK+D06tVLvXr1smUgvXv3ltPpVGlpqTfQeDwebdu2zfskVmpqqmpqalRRUaHk5GRJ0ptvvqnW1lalpKTYMi4AABB4bLsHZ//+/aqsrNT+/fvV0tKiyspKVVZW+nxnzYABA7Rp0yZJ31/9mDlzph599FG98sor2rlzpzIzMxUfH6/x48dLkgYOHKj09HRlZ2dr+/btevfddzVjxgxNnDjxjJ+gAgAA5vPrCo4/5s+fr7Vr13pfDx8+XJL01ltv6dprr5Uk7dq1S7W1td6aOXPmqL6+Xjk5OaqpqdHo0aNVXFyssLAwb8369es1Y8YMjRkzRsHBwbr55pu1fPlyu6YBAAACkO3fg3M+sut7cAAAgH0C8ntwAAAAOgoBBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwjm0/1XA+++HLmz0ezzkeCQAAOFM/vG+fyY8w/CwDzvHjxyVJCQkJ53gkAADAX8ePH1dkZOQpa36Wv0XV2tqqgwcPqnv37goKCurQvj0ejxISEnTgwAF+58pGrHPnYJ07B+vcOVjnzmPXWluWpePHjys+Pl7Bwae+y+ZneQUnODhYF198sa3niIiI4D+gTsA6dw7WuXOwzp2Dde48dqz16a7c/ICbjAEAgHEIOAAAwDgEnA7mcDi0YMECORyOcz0Uo7HOnYN17hysc+dgnTvP+bDWP8ubjAEAgNm4ggMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOGehsLBQiYmJCgsLU0pKirZv337K+o0bN2rAgAEKCwvTkCFDtGXLlk4aaWDzZ51XrFihq6++Wj169FCPHj3kcrlO++8F3/P37/MPNmzYoKCgII0fP97eARrC33WuqanR9OnTFRcXJ4fDocsvv5z/d5wBf9d52bJl6t+/v7p166aEhATNmjVL3333XSeNNjC9/fbbuvHGGxUfH6+goCC9/PLLpz2mrKxMv/rVr+RwONSvXz+tWbPG9nHKgl82bNhghYaGWqtWrbI++eQTKzs724qKirKqq6vbrH/33XetkJAQa/Hixdann35qPfjgg1bXrl2tnTt3dvLIA4u/63zbbbdZhYWF1ocffmhVVVVZU6ZMsSIjI62vvvqqk0ceWPxd5x/s3bvXuuiii6yrr77auummmzpnsAHM33VuaGiwRowYYY0bN8565513rL1791plZWVWZWVlJ488sPi7zuvXr7ccDoe1fv16a+/evdbrr79uxcXFWbNmzerkkQeWLVu2WA888ID10ksvWZKsTZs2nbJ+z549Vnh4uJWbm2t9+umn1tNPP22FhIRYxcXFto6TgOOnkSNHWtOnT/e+bmlpseLj462CgoI262+99VYrIyPDpy0lJcX6z//8T1vHGej8Xed/1dzcbHXv3t1au3atXUM0wtmsc3NzszVq1Cjr2WeftSZPnkzAOQP+rvOf/vQnq0+fPlZjY2NnDdEI/q7z9OnTreuvv96nLTc317rqqqtsHadJziTgzJkzx/rlL3/p0zZhwgQrLS3NxpFZFh9R+aGxsVEVFRVyuVzetuDgYLlcLpWXl7d5THl5uU+9JKWlpbVbj7Nb53914sQJNTU1qWfPnnYNM+Cd7To/8sgjiomJ0dSpUztjmAHvbNb5lVdeUWpqqqZPn67Y2FgNHjxYjz/+uFpaWjpr2AHnbNZ51KhRqqio8H6MtWfPHm3ZskXjxo3rlDH/XJyr98Gf5Y9tnq2jR4+qpaVFsbGxPu2xsbH67LPP2jzG7Xa3We92u20bZ6A7m3X+V3PnzlV8fPxJ/1HhR2ezzu+8845WrlypysrKThihGc5mnffs2aM333xTkyZN0pYtW7R7927dfffdampq0oIFCzpj2AHnbNb5tttu09GjRzV69GhZlqXm5mbddddd+v3vf98ZQ/7ZaO990OPx6Ntvv1W3bt1sOS9XcGCcRYsWacOGDdq0aZPCwsLO9XCMcfz4cd1xxx1asWKFoqOjz/VwjNba2qqYmBg988wzSk5O1oQJE/TAAw+oqKjoXA/NKGVlZXr88cf1xz/+UTt27NBLL72kzZs3a+HChed6aOgAXMHxQ3R0tEJCQlRdXe3TXl1dLafT2eYxTqfTr3qc3Tr/YMmSJVq0aJHeeOMNDR061M5hBjx/1/mLL77Qvn37dOONN3rbWltbJUldunTRrl271LdvX3sHHYDO5u9zXFycunbtqpCQEG/bwIED5Xa71djYqNDQUFvHHIjOZp3nzZunO+64Q3feeackaciQIaqvr1dOTo4eeOABBQdzDaAjtPc+GBERYdvVG4krOH4JDQ1VcnKySktLvW2tra0qLS1Vampqm8ekpqb61EtSSUlJu/U4u3WWpMWLF2vhwoUqLi7WiBEjOmOoAc3fdR4wYIB27typyspK7/brX/9a1113nSorK5WQkNCZww8YZ/P3+aqrrtLu3bu9AVKS/v73vysuLo5w046zWecTJ06cFGJ+CJUWP9PYYc7Z+6CttzAbaMOGDZbD4bDWrFljffrpp1ZOTo4VFRVlud1uy7Is64477rDy8vK89e+++67VpUsXa8mSJVZVVZW1YMECHhM/A/6u86JFi6zQ0FDrxRdftA4dOuTdjh8/fq6mEBD8Xed/xVNUZ8bfdd6/f7/VvXt3a8aMGdauXbus1157zYqJibEeffTRczWFgODvOi9YsMDq3r279fzzz1t79uyxtm7davXt29e69dZbz9UUAsLx48etDz/80Prwww8tSdbSpUutDz/80Pryyy8ty7KsvLw864477vDW//CY+P33329VVVVZhYWFPCZ+vnr66aetSy65xAoNDbVGjhxpvf/++95911xzjTV58mSf+r/85S/W5ZdfboWGhlq//OUvrc2bN3fyiAOTP+t86aWXWpJO2hYsWND5Aw8w/v59/mcEnDPn7zq/9957VkpKiuVwOKw+ffpYjz32mNXc3NzJow48/qxzU1OT9dBDD1l9+/a1wsLCrISEBOvuu++2/vGPf3T+wAPIW2+91eb/b39Y28mTJ1vXXHPNScckJSVZoaGhVp8+fazVq1fbPs4gy+I6HAAAMAv34AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAHeLtt9/WjTfeqPj4eAUFBenll1/2uw/LsrRkyRJdfvnlcjgcuuiii/TYY4/53Q8/tgkAADpEfX29hg0bpv/4j//Qv//7v59VH7/73e+0detWLVmyREOGDNGxY8d07Ngxv/vhm4wBAECHCwoK0qZNmzR+/HhvW0NDgx544AE9//zzqqmp0eDBg/XEE0/o2muvlSRVVVVp6NCh+vjjj9W/f/+fdH4+ogIAAJ1ixowZKi8v14YNG/TRRx/plltuUXp6uj7//HNJ0quvvqo+ffrotddeU+/evZWYmKg777zzrK7gEHAAAIDt9u/fr9WrV2vjxo26+uqr1bdvX913330aPXq0Vq9eLUnas2ePvvzyS23cuFHr1q3TmjVrVFFRod/85jd+n497cAAAgO127typlpYWXX755T7tDQ0N+sUvfiFJam1tVUNDg9atW+etW7lypZKTk7Vr1y6/PrYi4AAAANvV1dUpJCREFRUVCgkJ8dl34YUXSpLi4uLUpUsXnxA0cOBASd9fASLgAACA88rw4cPV0tKiw4cP6+qrr26z5qqrrlJzc7O++OIL9e3bV5L097//XZJ06aWX+nU+nqICAAAdoq6uTrt375b0faBZunSprrvuOvXs2VOXXHKJbr/9dr377rt66qmnNHz4cB05ckSlpaUaOnSoMjIy1NraqiuuuEIXXnihli1bptbWVk2fPl0RERHaunWrX2Mh4AAAgA5RVlam66677qT2yZMna82aNWpqatKjjz6qdevW6euvv1Z0dLSuvPJKPfzwwxoyZIgk6eDBg7rnnnu0detWXXDBBbrhhhv01FNPqWfPnn6NhYADAACMw2PiAADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABjn/wMHnGJAx4E6FAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Frequency Shift\n",
    "N = len(x)\n",
    "f_o = -57e3 # amount we need to shift by\n",
    "t = np.arange(N)/sample_rate # time vector\n",
    "plt.figure()\n",
    "plt.plot(np.exp(2j*np.pi*f_o*t))\n",
    "x = x * np.exp(2j*np.pi*f_o*t) # down shift\n",
    "plt.figure()\n",
    "plt.plot(x)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we frequency shift down by 57 kHz, using the e^{j2 \\pi f_ot} trick we learned in the Synchronization chapter where f_o is the frequency shift in Hz and t is just a time vector, the fact it starts at 0 isn’t important, what matters is that it uses the right sample period (which is inverse of sample rate). As an aside, because it’s a real signal being fed in, it doesn’t actually matter if you use a -57 or +57 kHz because the negative frequencies match the positive, so either way we are going to get our RDS shifted to 0 Hz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Low-Pass Filter to isolate RDS\n",
    "taps = firwin(numtaps=101, cutoff=7.5e3, fs=sample_rate)\n",
    "x = np.convolve(x, taps, 'valid')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we must filter out everything besides RDS. Since we have RDS centered at 0 Hz, that means a low-pass filter is the one we want. We use firwin() to design an FIR filter (i.e., find the taps), which just needs to know how many taps we want the filter to be, and the cutoff frequency. The sample rate must also be provided or else the cutoff frequency doesn’t make sense to firwin. The result is a symmetric low-pass filter, so we know the taps are going to be real numbers, and we can apply the filter to our signal using a convolution. We choose 'valid' to get rid of the edge effects of doing convolution, although in this case it doesn’t really matter because we are feeding in such a long signal that a few weird samples on either edge isn’t going to throw anything off."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Decimeate by 10\n",
    "# Decimate by 10, now that we filtered and there wont be aliasing\n",
    "x = x[::10]\n",
    "sample_rate /= 10 # New rate is now 25e3"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Any time you filter down to a small fraction of your bandwidth (e.g., we started with 125 kHz of real bandwidth and saved only 7.5 kHz of that), it makes sense to decimate. Recall the beginning of the IQ Sampling chapter where we learned about the Nyquist Rate and being able to fully store band-limited information as long as we sampled at twice the highest frequency. Well now that we used our low-pass filter, our highest frequency is about 7.5 kHz, so we only need a sample rate of 15 kHz. Just to be safe we’ll add some margin and use a new sample rate of 25 kHz (this ends up working well mathematically later on).\n",
    "\n",
    "We perform the decimation by simply throwing out 9 out of every 10 samples, since we previously were at a sample rate of 250 kHz and we want it to now be at 25 kHz. This might seem confusing at first, because throwing out 90% of the samples feels like you are throwing out information, but if you review the IQ Sampling chapter you will see why we are not actually losing anything, because we filtered properly (which acted as our anti-aliasing filter) and reduced our maximum frequency and thus signal bandwidth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resample to 19kHz\n",
    "x = resample_poly(x, 19, 25) # up, down\n",
    "sample_rate = 19e3"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the Pulse Shaping chapter we solidified the concept of “samples per symbol”, and learned the convenience of having an integer number of samples per symbol (a fractional value is valid, just not convenient). As mentioned earlier, RDS uses BPSK transmitting 1187.5 symbols per second. If we continue to use our signal as-is, sampled at 25 kHz, we’ll have 21.052631579 samples per symbol (pause and think about the math if that doesn’t make sense). So what we really want is a sample rate that is an integer multiple of 1187.5 Hz, but we can’t go too low or we won’t be able to “store” our full signal’s bandwidth. In the previous subsection we talked about how we need a sample rate of 15 kHz or higher, and we chose 25 kHz just to give us some margin.\n",
    "\n",
    "Finding the best sample rate to resample to comes down to how many samples per symbol we want, and we can work backwards. Hypothetically, let us consider targeting 10 samples per symbol. The RDS symbol rate of 1187.5 multiplied by 10 would give us a sample rate of 11.875 kHz, which unfortunately is not high enough for Nyquist. How about 13 samples per symbol? 1187.5 multiplied by 13 gives us 15437.5 Hz, which is above 15 kHz, but quite the uneven number. How about the next power of 2, so 16 samples per symbol? 1187.5 multiplied by 16 is exactly 19 kHz! The even number is less of a coincidence and more of a protocol design choice.\n",
    "\n",
    "To resample from 25 kHz to 19 kHz, we use resample_poly() which upsamples by an integer value, filters, then downsamples by an integer value. This is convenient because instead of entering in 25000 and 19000 we can use 25 and 19. If we had used 13 samples per symbol by using a sample rate of 15437.5 Hz, we wouldn’t be able to use resample_poly() and the resampling process would be much more complicated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8a80508880>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Time Synchronization (Symbol-Level)\n",
    "# Symbol sync, using what we did in sync chapter\n",
    "samples = x # for the sake of matching the sync chapter\n",
    "samples_interpolated = resample_poly(samples, 32, 1) # we'll use 32 as the interpolation factor, arbitrarily chosen, seems to work better than 16\n",
    "sps = 16\n",
    "mu = 0.01 # initial estimate of phase of sample\n",
    "out = np.zeros(len(samples) + 10, dtype=np.complex64)\n",
    "out_rail = np.zeros(len(samples) + 10, dtype=np.complex64) # stores values, each iteration we need the previous 2 values plus current value\n",
    "i_in = 0 # input samples index\n",
    "i_out = 2 # output index (let first two outputs be 0)\n",
    "while i_out < len(samples) and i_in+32 < len(samples):\n",
    "    out[i_out] = samples_interpolated[i_in*32 + int(mu*32)] # grab what we think is the \"best\" sample\n",
    "    out_rail[i_out] = int(np.real(out[i_out]) > 0) + 1j*int(np.imag(out[i_out]) > 0)\n",
    "    x = (out_rail[i_out] - out_rail[i_out-2]) * np.conj(out[i_out-1])\n",
    "    y = (out[i_out] - out[i_out-2]) * np.conj(out_rail[i_out-1])\n",
    "    mm_val = np.real(y - x)\n",
    "    mu += sps + 0.01*mm_val\n",
    "    i_in += int(np.floor(mu)) # round down to nearest int since we are using it as an index\n",
    "    mu = mu - np.floor(mu) # remove the integer part of mu\n",
    "    i_out += 1 # increment output index\n",
    "x = out[2:i_out] # remove the first two, and anything after i_out (that was never filled out)\n",
    "\n",
    "plt.plot(np.real(x), np.imag(x), '.')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
